Digital Libraries and Archives - IRCDL 2011

Communications in Computer and Information Science

249

Maristella Agosti Floriana Esposito Carlo Meghini Nicola Orio (Eds.)

Digital Libraries and Archives 7th Italian Research Conference, IRCDL 2011 Pisa, Italy, January 20-21, 2011 Revised Selected Papers

13

Volume Editors Maristella Agosti University of Padua, Department of Information Engineering Via Gradenigo, 6/a, 35131 Padua, Italy E-mail: [email protected] Floriana Esposito University of Bari, Department of Computer Science Via E. Orabona, 4, 70126 Bari, Italy E-mail: [email protected] Carlo Meghini Institute for Information Science and Technologies Italian National Research Council (ISTI-CNR) Via G. Moruzzi, 1, 56124 Pisa, Italy E-mail: [email protected] Nicola Orio University of Padua, Department of Information Engineering Via Gradenigo, 6/a, 35131 Padua, Italy E-mail: [email protected]

ISSN 1865-0929 e-ISSN 1865-0937 ISBN 978-3-642-27301-8 e-ISBN 978-3-642-27302-5 DOI 10.1007/978-3-642-27302-5 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011943309 CR Subject Classification (1998): H.3, H.5, H.4, J.1, H.2, H.2.8

© Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

IRCDL is a yearly event for Italian researchers working on Digital Libraries-related topics. This year the focus of IRCDL was on information access and interoperability. In fact Digital Library systems are starting to be mature, but it is time to give a more effective and personalized access to information to users and to improve interoperability among heterogeneous available systems. The Semantic Web is emerging as a set of tools and methods that can respond to many of the Digital Library requirements, and linked data, in particular, are likely to play a central role in interoperability. The IRCDL conferences were launched and initially sponsored by DELOS, an EU FP6 Network of Excellence on Digital Libraries together with the Department of Information Engineering of the University of Padua, Italy. Over the years IRCDL has become a self-sustainable event supported by the Italian Digital Library Research Community. The volume contains the revised accepted papers from among those presented at the 7th Italian Research Conference on Digital Libraries (IRCDL 2011), which was held in the Institute for Information Science and Technologies of the Italian National Research Council (ISTI-CNR), Pisa, Italy, during January 20–21, 2011. The aim of IRCDL is to bring together the Italian research community interested in the diversified methods and techniques that allow the building and operation of Digital Libraries. A national Program Committee was set up composed of 15 members, with representatives of the most active Italian research groups in Digital Libraries. The papers accepted for inclusion in this volume are an expanded version of the papers presented orally. These papers were reviewed again and the results of the selection are the papers appearing in the volume. The topics covered are related to the different aspects needed to support information access and interoperability, including: • • • • • • • • • • •

System Interoperability and Data Integration Formal and methodological foundations of Digital Libraries Semantic Web and Linked Data for Digital Libraries Multilingual Information Access Digital Library Infrastructures Metadata Creation and Management Search Engines for Digital Library Systems Evaluation and Log Data Handling Audio/Visual and Non-traditional Objects User Interfaces and Visualization Digital Library quality

Taking into consideration that the Italian research community is involved in different relevant projects largely related to the area of Digital Libraries, it was

VI

Preface

decided to report in the volume the most recent results that have been reached in the diverse projects so as to present them to the international community, since this year, too, the IRCDL proceedings are published in the Springer CCIS series. We would like to thank those institutions and individuals who have made the conference and this volume possible: • The Program Committee members • The Institute for Information Science and Technologies of the Italian National Research Council (ISTI-CNR) in Pisa, and the members of the same institute who contributed to the organization of the event, namely, Francesca Borri, Alessandro Nardi, and Catherine Bosio • The Department of Information Engineering of the University of Padua A call for participation for IRCDL 2012 will be circulated, but meanwhile we invite all researchers interested in Digital Libraries to start thinking about possible contributions to next year’s conference. September 2011

Maristella Agosti Floriana Esposito Carlo Meghini Nicola Orio

Organization

General Chair Costantino Thanos

ISTI CNR, Pisa

Program Chairs Carlo Meghini Floriana Esposito

ISTI CNR, Pisa University of Bari

Program Committee Maristella Agosti Giuseppe Amato Marco Bertini Leonardo Candela Tiziana Catarci Alberto Del Bimbo Stefano Ferilli Nicola Ferro Maria Guercio Fausto Rabitti Pasquale Savino Anna Maria Tammaro Letizia Tanca Carlo Tasso

University of Padua ISTI CNR, Pisa University of Florence ISTI CNR, Pisa University of Rome “La Sapienza” University of Florence University of Bari University of Padua University of Urbino “Carlo Bo” ISTI CNR, Pisa ISTI CNR, Pisa University of Parma Politecnico di Milano University of Udine

Proceedings Chair Nicola Orio

University of Padua

Organizing Committee Institute for Information Science and Technologies of the Italian National Research Council (ISTI-CNR) - Pisa: Francesca Borri Alessandro Nardi Catherine Bosio

VIII

Organization

Supporting Institutions IRCDL 2011 benefited from the support of the following organizations: Institute for Information Science and Technologies of the Italian National Research Council (ISTI-CNR), Pisa, Italy Department of Information Engineering, University of Padua, Italy

Table of Contents

Selected Papers Probabilistic Inference over Image Networks . . . . . . . . . . . . . . . . . . . . . . . . . Claudio Taranto, Nicola Di Mauro, and Floriana Esposito

1

A Keyphrase-Based Paper Recommender System . . . . . . . . . . . . . . . . . . . . Felice Ferrara, Nirmala Pudota, and Carlo Tasso

14

Accessing Music Digital Libraries by Combining Semantic Tags and Audio Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Riccardo Miotto and Nicola Orio Improving User Stereotypes through Machine Learning Techniques . . . . . Teresa M.A. Basile, Floriana Esposito, and Stefano Ferilli Displaying Phonological Diachronic Changes through a Database Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marta Manfioletti and Mattia Nicchio A Digital Library of Grammatical Resources for European Dialects . . . . . Maristella Agosti, Birgit Alber, Giorgio Maria Di Nunzio, Marco Dussin, Diego Pescarini, Stefan Rabanus, and Alessandra Tomaselli Taxonomy Based Notification Service for the ASSETS Digital Library Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jitao Yang, Tsuyoshi Sugibuchi, and Nicolas Spyratos SIAR: A User-Centric Digital Archive System . . . . . . . . . . . . . . . . . . . . . . . Maristella Agosti, Nicola Ferro, Andreina Rigon, Gianmaria Silvello, Erilde Terenzoni, and Cristina Tommasi

26

38

49

61

75

87

Relevant Projects ASIt: A Grammatical Survey of Italian Dialects and Cimbrian: Fieldwork, Data Management, and Linguistic Analysis . . . . . . . . . . . . . . . . Maristella Agosti, Birgit Alber, Paola Beninc` a, Giorgio Maria Di Nunzio, Marco Dussin, Riccardo Miotto, Diego Pescarini, Stefan Rabanus, and Alessandra Tomaselli

100

X

Table of Contents

ASSETS: Advanced Service Search and Enhancing Technological Solutions for the European Digital Library . . . . . . . . . . . . . . . . . . . . . . . . . . Nicola Aloia, Cesare Concordia, and Carlo Meghini Computational Models Enhancing Semantic Access to Digital Repositories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Floriana Esposito, Nicola Di Mauro, Claudio Taranto, and Stefano Ferilli The CULTURA Project: CULTivating Understanding and Research through Adaptivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maristella Agosti and Nicola Orio Project D.A.M.A.: Document Acquisition, Management and Archiving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michelangelo Ceci, Corrado Loglisci, Stefano Ferilli, and Donato Malerba DDTA - Digitalisation of Districts in the Textile and Clothing Sector . . . Floriana Esposito, Stefano Ferilli, Nicola Di Mauro, Teresa M.A. Basile, and Marenglen Biba DOMINUSplus - DOcument Management INtelligent Universal System (plus) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stefano Ferilli, Floriana Esposito, Teresa M.A. Basile, Domenico Redavid, and Incoronata Villani

104

107

111

115

119

123

Europeana v1.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nicola Aloia, Cesare Concordia, and Carlo Meghini

127

EuropeanaConnect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Franco Crivellari, Graziano Deambrosis, Giorgio Maria Di Nunzio, Marco Dussin, and Nicola Ferro

130

MBlab: Molecular Biodiversity Laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . Corrado Loglisci, Annalisa Appice, Michelangelo Ceci, Donato Malerba, and Floriana Esposito

132

A Personalized Intelligent Recommender and Annotator TEStbed for Text-Based Content Retrieval and Classification: The PIRATES Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Felice Ferrara and Carlo Tasso

136

Table of Contents

PROMISE – Participative Research labOratory for Multimedia and Multilingual Information Systems Evaluation . . . . . . . . . . . . . . . . . . . . . . . . Emanuele Di Buccio, Marco Dussin, Nicola Ferro, Ivano Masiero, and Gianmaria Silvello Cooperative Digital Asset Management in the Scientific Field: Strategies, Policies, Interoperability and Persistent Identifiers . . . . . . . . . . Maurizio Lancia, Roberto Puccinelli, Massimiliano Saccone, Marco Spasiano, and Luciana Trufelli Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

XI

140

144

161

Probabilistic Inference over Image Networks Claudio Taranto, Nicola Di Mauro, and Floriana Esposito Department of Computer Science, University of Bari “Aldo Moro” Via E. Orabona, 4 – 70125 Bari, Italy {claudio.taranto,ndm,esposito}@di.uniba.it

Abstract. Digital Libraries contain collections of multimedia objects providing services for the management, sharing and retrieval. Involved objects have two levels of complexity: the former refers to the inner object complexity while the latter takes into account the implicit/explicit relationships among objects. Traditional machine learning classifiers do not consider the relationships among objects assuming them independent and identically distributed. Recently, link-based classification methods have been proposed, that try to classify objects exploiting their relationships (links). In this paper, we deal with objects corresponding to digital images, even if the proposed approach can be naturally applied to different kind of multimedia objects. Relationships can be expressed among the features of the same image or among features belonging to different images. The aim of this work is to verify whether a link-based classifier based on a Statistical Relational Learning (SRL) language can improve the accuracy of a classical k-nearest neighbour approach. Experiments will show that the modelling of the relationships in a real-word dataset using a SRL model reduces the classification error.

1

Introduction

Digital Libraries organized digital collections of multimedia objects available online in computer processable form [1]. These libraries also comprise services and infrastructures to manage, store, retrieve and share objects. Digital images represent a component of multimedia objects involved in the digital library universe. The Machine Learning scientific community has developed methods and models to address the emerging issues arising from the management of these data types. One of main issues concerns the image classification, which addresses the need to automate the process of assigning a class label to a given image. This problem has been mainly tackled by adopting a learning-based or a non-parametric classifier [2]. The former approach requires a training phase in which a learning system induces a model able to identify new unlabelled images, while the latter does not require a learning phase but adopts methods directly applicable to the images to be classified. Both learning approaches require a feature based representation of the images. Furthermore, non-parametric algorithms adopt a similarity measure defined on the feature space aiming to calculate the similarity degree between two images. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 1–13, 2011. c Springer-Verlag Berlin Heidelberg 2011 

2

C. Taranto, N. Di Mauro, and F. Esposito

Given D a set of images, the majority of the existing methods adopted for image classification try to estimate the class probability of a query image q ∈ D, P (Cq |D \{q}), assuming that all the images in D are independent and identically distributed (i.i.d.). In particular,  the probability P (Cq |D \ {q}) is assumed to be factored as in the following way i αP (Cq |xi ), with xi ∈ D. This paper proposes a Statistical Relational Learning (SRL) [3] method to exploit the relationships among images in order to improve the image classification accuracy by means of a link-based classifier [4,5]. The main idea is to assume that the images xi ∈ D are not mutually independent and to try to elicit the hidden information representing the probabilistic connections between two images taking into account the possible relationships. To reach this goal, images are represented by means of a complex probabilistic network, where each image corresponds to a node and the connection degree among images is represented by a probabilistic edge. The relationship degree among images may be computed adopting a similarity measure based on their feature based representation. Correlograms [6], a statistics expressing how the colors of an image are spatially correlated, are used in this paper as features representing the images. The main goal of this paper is to verify whether modelling image classification problem using a SRL language can improve the accuracy of a classical k-nearest neighbour (k-NN) approach. The focus is not on proposing a new image classification algorithm, but on showing that a SRL model can improve a classical classification method. We adopted the probabilistic logic ProbLog [7] as SRL model to describe the structure of the probabilistic network arising from the abstraction process we adopted to represent an image collection. The experimental results obtained from a real world dataset confirmed the validity of the proposed approach.

2

Related Work

The increasing interest in the last years about the problem of image classification provided a lot of models. Here we briefly review some of them that may be related to our proposed statistical relational approach. The Bag of Words model [8] (BOW) has been inherited from the area of document analysis. It describes each image as a collection of features, without analysing neither its geometric structure nor features’ position in the image. Given a shared vocabulary for the allowed features, named codebook, each image is then encoded as a distribution of codebook names. In order to classify new images, Probabilistic Latent Semantic Analysis (pLSA) [9], Latent Dirichlet Allocation (DLA) [10] or Vector Space Model (VSM) [11] may be used. The limitation of this approach is that all the spatial relationships among the features are ignored. Part-based methods [12] try to solve the problems of the BOW model. The main idea is to consider an object consisting of a set of N parts connected to each other and describing each component considering specific geometric characteristics (e.g., the constellation model [13]). The adopted feature model, such as color histogram, correlogram and sift, will be enriched by considering location and dimension

Probabilistic Inference over Image Networks

3

properties. In the training phase the system induces a model that will be able to classify objects considering their parts. In order to model the relationships among the images, or among the objects within an image, a graph based representation is usually adopted. This structure provides a powerful model [14], where nodes store local information while the edges encode the spatial relationships, with a companion set of tools, inherited from the graph theory, useful to manage and inspect it. Given a graph based representation, collective classification methods [15] can be used. In collective classification relationships among objects must be taken into account in order to enhance the predictive accuracy of the model: the labelling of an object should depend on the labels of its neighbours. Weighted-Vote Relational Neighbor (WVRN) [16], estimates the probability of a class given a node by summing the probabilities of nodes in the neighbourhood of the same class. Class-Distribution Relational Neighbour (CDRN) [17] uses a vectorial representation of the neighbours by assigning to each node v of known label a vector whose elements represent the probability that a neighbour node have a given label. The Network-Only Bayes (NOB) classifier [18] adopts a naive Bayes approach to compute the label probabilities of a node, assumed to be independent, conditioned on the labels of its neighbours. Finally, the Network-Only LinkBased (NOLB) classifier [19] learns a multiclass logistic regression model using the label distribution in the neighbourhood of nodes with known labels. These methods are based on a link-based classification, but they consider the data as already represented as a network, and then relegating their use for dataset containing explicit relations. While, one of our goals is to elicit the relationships between objects, thereby transforming a set of i.i.d. data in a network and describe the connections found using a relational logic formalism.

3

Image Correlograms

There is a large number of features that may be used to describe an image. In [6] has been presented an approach, named color correlogram, that combines both global and local image information. This statistics describes how pixels with a given color are spatially distributed in an image, and it is generally more accurate and effective than histogram-based methods. However the time required to compute this statistics may be very high. In [20] has been proposed a new sampling method to approximate the distribution of correlograms proving that the computational time to compute the statistics may be reduce by taking high the similarity based accuracy. In this paper we used image correlograms as a feature representation, even if the generality of the proposed relational approach allows the adoption of any kind of feature. A correlogram is defined as a table indexed by color pairs, where the k-th entry for the component (i, j) specifies the probability of finding a pixel whose color is j away k pixel from a pixel whose color is i, where k is a distance chosen from a set D.

4

C. Taranto, N. Di Mauro, and F. Esposito

Let I be an n1 × n2 image, whose colors are quantised into m classes C = {c1 , ...., cm } (bins). Given a pixel pxy ∈ I, I(p) denotes its color and Ic  {pij ∈ I|I(p) = c}. In the following, we assume to use the L∞ -norm to measure the distance between two pixels. In particular, given px1 y1 and px2 y2 two pixels, their distance is computed as follows [6]: |px1 y1 − px2 y2 |  max{|x1 − x2 |, |y1 − y2 |}. The histogram h of an image I is defined as hci (I) = n1 n2 Prp∈I [p ∈ Ic ], for each color class ci ∈ C. Let d ∈ D be a distance, the correlogram of an image I is defined as follows: γc(d) (I) = Prp1 ∈Ici ,p2 ∈I [p2 ∈ Icj ||p1 − p2 | = d], i ,cj

(1)

where ci , cj ∈ C are two color classes. The size of the color correlogram matrix (d) (d) is |C|2 . A specialisation is the autocorrelogram defined as αc (I) = γc,c (I).

4

The Probabilistic Logic ProbLog

Among the many languages considered in SRL there are some based on Logic Programming such as ICL [21], PRISM [22], CLP(BN) [23] and ProbLog [7]. The representation and use of probability theory makes SRL algorithms suitable for combining domain knowledge and data, expressing relationships, avoiding overfitting a model to training data, and learning from incomplete datasets. The probabilistic formalism provides a natural treatment for the stochastic nature of some complex domains. As for classical probabilistic graphical models [24], such as Bayesian networks and Markov networks, statistical relational languages exploit the structure underlying many distributions we want to encode. The same structure often allows the distribution to be effectively used for inference, answering queries using the distribution as a model of the world. Finally, the SRL framework facilitates the effective learning from data of models providing a good approximation to a past experience. ProbLog is a probabilistic framework that extends Prolog with probabilistic facts and answers several kinds of probabilistic queries, which has been used for learning in the context of large networks where edges are labelled with probabilities. The ProbLog’s semantics is an instance of the distributional semantics defined in [25]. A ProbLog program defines a distribution over all its possible nonprobabilistic subprograms. Facts are labelled with probabilities and treated as mutually independent random variables indicating whether or not the corresponding fact belongs to a randomly sampled program. The success probability of a query is defined as the probability that it succeeds in such a random subprogram. A ProbLog program T = p1 :: c1 , . . . , pn :: cn ∪ BK defines a probability distribution over subprograms L ⊆ LT = {c1 , . . . , cn }:   P (L|T ) = pi (1 − pi ). ci ∈L

ci ∈LT \L

Probabilistic Inference over Image Networks

5

While in Prolog the result of query is binary, i.e. success or failure, in ProbLog the result of a query is the probability of success, i.e. the probability that the query succeeds in a random sample. In particular, the success probability Ps (q|T ) of a query q in a ProbLog program T is defined as Ps (q|T ) =



P (q|L) · P (L|T ),

L⊆LT

where P (q|L) = 1 if there exists a θ substitution such that L ∪ BK |= qθ, and P (q|L) = 0 otherwise. The explanation probability Px (q|T ) is defined as the probability of the most likely explanation of the proof of the query q: Px (q|T ) = maxe∈E(q) P (e|T ) = maxe∈E(q)



pi ,

where E(q) is the set of all explanations for the query q. Example 1. The following probabilistic ProbLog facts represent the correlations (or similarities) among some images reported in the graph. Each fact p::edge(a,b) denotes the probability p of seeing the edge between a and b in a randomly sampled world. The last clause asserts that the two images A and B are similar whether there is an edge from A to B or viceversa. 0.7::edge(a,d). 0.9::edge(d,b). 0.8::edge(b,c). 0.6::edge(c,a). 0.5::edge(a,e). 0.5::edge(e,d). 0.7::edge(e,f). 0.6::edge(d,f). sim(A,B) :edge(A,B) ; edge(B,A). The ProbLog framework includes different inference methods and its implementation is based on the use of tries and reduced ordered binary decision diagrams (ROBDDs). The execution of ProbLog programs uses SLD-resolution to collect all the proofs for a query. In the case of exact inference, for each successful proof of the query, the probabilistic facts used in the proof are gathered and BDDs are used to solve the disjoint sum problem and to obtain the correct probability of the query. In particular, since probability computation and learning in ProbLog are based on propositional logic, compressing Boolean (propositional) functions by BDDs accelerates them. Another solution adopted to overcome the combinatorial explosion of the BDD in ProbLog is to use approximation methods for inference as those proposed in [26,27].

6

C. Taranto, N. Di Mauro, and F. Esposito

5

Image Network Representation

The methods usually used for the image classification task assume the images as independent and identically distributed. Here, we want to show that adopting a SRL language modelling a complex network may be used to solve the image classification problem providing better results than those obtained with classical methods. The first step corresponds to choose a representation language for the images. This is generally done by extracting for each image x ∈ D some features. In this paper, we describe each image I with its corresponding correlogram statistics (d) γci ,cj (I), computed as reported in Equation 1. After the feature extraction process, a pairwise image comparison approach should be defined. In particular, it is necessary to define a measure able to identify similar images. Given a set of K classes, denoted as Ck , a discriminant [28] is a function that takes as input an observation x ∈ D and classifies it as belonging to one of the K classes. In the general case of K > 2 classes, we consider a single K-class discriminant comprising K linear functions φk and then assigning an observation x to a class Ck if φk (x) > φj (x) for all j = k. The well known k-NN technique represents one of the most adopted nonparametric approaches to estimate the class of a given unseen observation. In particular, to classify a new observation, the k-NN method identifies k nearest observations from the training data and then assigns the new observation to the class having the largest number of representatives among this set [28]. The particular case of k = 1 corresponds to the nearest neighbour rule, where a test observation is simply assigned to the same class as the nearest observation from the training set. In order to compute the nearest neighbours, a distance function between two observations must be used. For instance, the distance between two  1/p observations x, y ∈ D may be given by dist(x, y) = ( i |xi − yi |p ) , where xi and yi are the components of the representation of the observations x and y. The number of edges in the network built in this way grows as the degree of each node increases. The degree of a node x corresponds to the number of edges connecting similar images to x. Since we have to build a probabilistic network (edges labelled with probabilities), given a distance function d, returning a value between 0 and 1, it is possible to build the network, where each image is represented as a node, and each edge connecting two similar images x and y is labelled with the value 1 − d(x, y). In a classical approach, where there is not an underlying network structure, computing the similarity between two images x and y corresponds to apply the distance function only to x and y. In this way we consider x and y as independent, ignoring, for instance, the fact that there may be an image z that is similar to both x and y. The idea is to have a network that allows us to exploit all the available connections among the images belonging to the data set. After having explained how to build the probabilistic network, it is natural to convert it to a ProbLog program as that reported in the Example 1. In particular, each network’s edge corresponds to a probabilistic fact p::edge/2, where p is the similarity degree between the two images that are the arguments of the edge

Probabilistic Inference over Image Networks

7

predicate. Hence, given the ProbLog program, it is possible to solve a query q asking the similarity degree between two nodes by adopting an inference method. In order to exploit the relations among the images, the ProbLog program has been enriched with the following definition. path(A,B,K) :sim(A,B). path(A,B,K) :K > 1, sim(A,C), K1 is K - 1, path(C,B,K1). The query path(x,y,k) is true for all the possible paths, with a length at most equal to k, starting from the node x and ending to the node y. Since the probability of this query is calculated collecting all its proofs (all the possible paths) then it corresponds to the similarity between two nodes computed considering the subnetwork containing the nodes related to those involved in the query. The value of the k parameter expresses the dimension of the considered subnetwork: k=1 corresponds to a classical approach. Example 2. Given T the ProbLog program reported in the Example 1 enriched with the definition of the predicate path/3, the probability of the query predicate path(a,b,1) is 0, since there are no edges directly connecting a and b. While the success probability of the query path(a,b,2) is the sum of the probabilities of all the subprograms containing edges connecting a and b: Ps (path(a, b, 2)|T ) = P ({edge(a, c), edge(c, b)}|T )+ P ({edge(a, d), edge(d, b), edge(e, f)}|T )+ P ({edge(a, c), edge(e, d), edge(c, b)}|T ) + . . .

6

Experiments

In this section we present some experimental results to asses whether the SRL proposed approach works better than the classical one. In order to do this, the Caltech 101 dataset [29] provided by the California Institute of Technology has been used. We chosen five classes (Airplaines, Faces, Watch, Motorbikes and Leopards) and 30 images have been randomly selected for each class obtaining a dataset of 150 instances. Each image has been represented by the correlograms calculated on given distances corresponding to 1, 3, 5 or 7. Having a correlogrambased representation, the distance between two images, x and y, is computed as follows, as reported in [6]: dc (x, y) = |x − y| 



|γci ,cj (x) − γci ,cj (y)|

ci ,cj ∈C,d∈D

1 + γci ,cj (x) + γci ,cj (y)

(d)

(d)

(d)

(d)

.

(2)

8

C. Taranto, N. Di Mauro, and F. Esposito

Let D be a set of images labelled with one of the k classes Q = {q1 , . . . , qk }, and #qi = |{x ∈ D|cl(x) = qi }|, where cl is a function returning the class of an image. To rank images with respect to a query image q the similarity is calculated as 1 − dc (xi , q). As in classical k-NN-based approaches, for each query image q, the error of the ranking result on the first n ranked images is calculated using the following formula n  n errq = 1(cl(xt ) = cl(q)), (3) t=1

where 1 is the indicator function that evaluates to 1 if cl(xt ) = cl(q) and 0 otherwise, and xt are the first n elements of the ranked images list with respect to q. In our experiment #qi = 30 for each of the five selected classes. Furthermore, the error, computed using the Equation 3, refers to the first 29 (n) ranked images. In particular, when errqn = 0 means that all the first n images in the ranked images list have the same class of q. In order to evaluate the SRL based approach, its results are compared to that obtained by the classical method. As already said, the probabilistic network is built using the function reported in Equation 2. We have to decide how much edges, connecting similar images, to insert in the network for each node (image). Given an image x, the α parameter represents the number of the best similar images to x (its neighbours). For each of these neighbours the similarity probability is computed and added to the probabilistic fact edge/2 in the ProbLog program. In the following experiments a value of α set to 2, 3, 4 or 5 has been used. Figure 1 shows (on the left) the probabilistic network constructed by setting to 4 the number of neighbours for each image. In the graph all the nodes filled with the same color, from a grayscale, correspond to the same class. A portion of the network (on the right) with the nodes represented by the corresponding image is depicted in the same figure. We can note that although only the four most similar images are considered as neighbourhood, the network complexity is evident. The application of a classical approach neglects all the relationships shown by the network resulting in a less accurate classification result. The validation of the proposed SRL approch has been done by comparing each image with all the others and ranking the results adopting the ProbLog exact inference. Using the α parameter involves the insertion of only a subset of all the possible connections. Indeed, in the case of few connections, given two image x and y, the ProbLog success probability for path(x,y,1) may be 0, because there may be no link between x and y in the network. The absence of the edge between x and y means that y is not present among the first best α similar images (and viceversa). A similar problem concerns the choice of the parameter k in the query path/3. In order to solve the problem of the 0 estimated probabilities, due to connections’ absence, the error has been computed considering the first images in the ranked images list whose probability is not equal to 0. In particular, let z < n the position in the ranked images list such that all the images xi , i ≤ z, have

Probabilistic Inference over Image Networks

9

Fig. 1. An image network where each image is connected to the four most similar neighbours

a probability not equal to 0, and z + 1 the position in the list of the ranked images corresponding to the first image for which ProbLog provides a success probability equal to 0. The errors obtained in the first z images has been used to estimate the total error probability over all the n > z images as follows: n

q = srlerr

k−1 (n k

where srlerrqz =

z 

− z) + (z · srlerrqz ) , n

(4)

1(cl(xt ) = cl(q)).

(5)

t=1

In particular in Equation 4, k−1 (n − z) represents the expected number of erk rors in the remaining (n − z) images, where we assumed that classifications are uniformly distributed among the k classes ( k−1 is the probability to incorrectly k classify an image).

10

C. Taranto, N. Di Mauro, and F. Esposito

Table 1 displays the result obtained on the Airplanes class. In the column headings, d is the distance used for computing the correlogram matrix, α is the number of neighbors selected for each image to build network, and errqn is the number of errors obtained in the first 29 best ranked results with a classical approach. For each value of the k parameter of the path query solved by ProbLog, z is the position in the ranked list corresponding to the last image with rank z q different to 0, srlerrqz is the number of error in the first z positions, and srlerr is the expected number of errors in the first 29 positions. As we can see, the obtained errors with the SRL approach is inversely proportional to both the α and k parameters, and they are fewer than those obtained with the classical method. This result proves the validity of our approach and the advantage of using an SRL language. Table 1. Results on the Airplanes class d α errqn

1

3

5

7

2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5

0.41

0.41

0.42

0.41

k=2 z srlerrqz 8.40 0.10 12.73 0.13 20.73 0.17 25.10 0.22 7.40 0.06 13.90 0.14 18.63 0.22 23.83 0.25 7.73 0.06 12.43 0.13 19.37 0.18 24.70 0.24 7.27 0.07 12.87 0.13 20.97 0.21 25.27 0.28

n

q srlerr 0.60 0.51 0.35 0.30 0.61 0.48 0.42 0.35 0.60 0.51 0.38 0.32 0.62 0.50 0.38 0.35

k=3 z srlerrqz 16.67 0.19 25.20 0.27 28.93 0.19 29.00 0.19 14.17 0.10 25.90 0.22 28.77 0.28 29.00 0.25 14.27 0.12 24.30 0.24 28.90 0.21 29.00 0.22 12.97 0.13 24.73 0.27 29.00 0.22 29.00 0.24

n

q srlerr 0.45 0.34 0.19 0.19 0.46 0.28 0.28 0.25 0.47 0.33 0.21 0.22 0.50 0.34 0.22 0.24

k=4 z srlerrqz 25.43 0.27 29.00 0.27 29.00 0.12 29.00 0.16 20.63 0.15 28.90 0.21 29.00 0.26 29.00 0.22 22.17 0.21 28.97 0.27 29.00 0.18 29.00 0.20 19.17 0.24 28.87 0.29 29.00 0.16 29.00 0.19

n

q srlerr 0.34 0.27 0.12 0.16 0.34 0.21 0.26 0.22 0.35 0.27 0.18 0.20 0.43 0.29 0.16 0.19

Table 2 shows the results obtained on the other classes: Faces, Watch, Motorbikes and Leopard, where we take fixed the parameter k to 4. The errors obtained with other classes confirm the observations made on the class Airplanes. The computational time of the proposed approach is very high when compared to a classical k-NN. This is due to the ProbLog inference procedure that represents its bottleneck.

7

5

3

1

2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5

dα

0.25

0.25

0.27

0.33

errqn

Faces z serrqz 26.5 0.28 29.0 0.25 29.0 0.20 29.0 0.17 27.4 0.25 29.0 0.21 29.0 0.18 29.0 0.17 26.4 0.29 29.0 0.21 29.0 0.18 29.0 0.13 27.4 0.28 29.0 0.18 29.0 0.14 29.0 0.11

serr n q 0.33 0.25 0.20 0.17 0.28 0.21 0.18 0.17 0.33 0.21 0.18 0.13 0.31 0.18 0.14 0.11 0.76

0.75

0.75

0.72

errqn

Watch z serrqz 25.7 0.32 29.0 0.67 29.0 0.71 29.0 0.73 23.6 0.57 28.8 0.64 29.0 0.70 29.0 0.77 24.5 0.51 29.0 0.64 29.0 0.69 29.0 0.76 24.9 0.56 29.0 0.69 29.0 0.74 29.0 0.77 serr n q 0.64 0.67 0.71 0.73 0.61 0.65 0.70 0.77 0.55 0.64 0.69 0.76 0.60 0.69 0.74 0.77

Motorbikes errqn z serrqz serr n q 26.1 0.27 0.32 29.0 0.24 0.24 0.26 29.0 0.21 0.21 29.0 0.21 0.21 24.8 0.22 0.30 29.0 0.19 0.19 0.25 29.0 0.17 0.17 29.0 0.18 0.18 25.3 0.23 0.30 29.0 0.22 0.22 0.23 29.0 0.19 0.19 29.0 0.16 0.16 27.6 0.26 0.29 29.0 0.19 0.19 0.22 29.0 0.18 0.18 29.0 0.16 0.16 0.17

0.15

0.14

0.13

errqn

Leopard z serrqz 23.7 0.09 27.6 0.07 28.2 0.09 28.9 0.09 21.8 0.11 27.3 0.14 28.7 0.25 28.9 0.15 28.8 0.10 27.2 0.12 27.9 0.12 28.9 0.15 19.0 0.11 27.5 0.13 28.1 0.13 28.2 0.13

Table 2. Results on the Faces, Watch, Motorbikes and Leopard classes with k= 4 serr n q 0.22 0.10 0.11 0.09 0.28 0.15 0.25 0.15 0.30 0.16 0.14 0.15 0.35 0.16 0.15 0.15

Probabilistic Inference over Image Networks 11

12

7

C. Taranto, N. Di Mauro, and F. Esposito

Conclusion

In this paper we considered the image classification problem. In particular our goal was to see if it is possible to increase the classification accuracy obtained using a classical k-NN approach, where the relationships among the images are ignored. In order to capture the connections among images we have used a SRL language. Connections between two images have been weighted with a probability. Experiments have been conducted on a real-word data set comparing the classical method with respect to our presented approach. The results show the validity of the proposed method.

References 1. Candela, L., Castelli, D., Ferro, N., Ioannidis, Y., Koutrika, G., Meghini, C., Pagano, P., Ross, S., Soergel, D., Agosti, M., Dobreva, M., Katifori, V., Schuldt, H.: The DELOS Digital Library Reference Model. Foundations for Digital Libraries (2007) 2. Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Computer Vision and Pattern Recognition, pp. 1–8 (2008) 3. Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. The MIT Press (2007) 4. Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explorations Newsletter 7, 3–12 (2005) 5. Getoor, L.: Link-based Classification. In: Maulik, U., Holder, L., Cook, D. (eds.) Advanced Methods for Knowledge Discovery from Complex Data. Springer, Heidelberg (2005) 6. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997) 7. Raedt, L.D., Kimmig, A., Toivonen, H.: Problog: a probabilistic prolog and its application in link discovery. In: Proceedings of 20th International Joint Conference on Artificial Intelligence, pp. 2468–2473. AAAI Press (2007) 8. Nowak, E., Jurie, F., Triggs, B.: Sampling Strategies for Bag-of-Features Image Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006) 9. Hofmann, T.: Probabilistic latent semantic analysis. In: Proceeding of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1995) 10. Blei, D.M., Ng, A.Y., Jordan, M.I., Lafferty, J.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003) 11. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communication ACM 18, 613–620 (1975) 12. Burl, M.C., Weber, M., Perona, P.: A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998, Part II. LNCS, vol. 1407, pp. 628–641. Springer, Heidelberg (1998) 13. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271 (2003)

Probabilistic Inference over Image Networks

13

14. Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence, 265–298 (2004) 15. Sen, P., Namata, G.M., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29, 93–106 (2008) 16. Macskassy, S.A., Provost, F.: A simple relational classifier. In: Proceedings of the Second Workshop on Multi-Relational Data Mining, pp. 64–76 (2003) 17. Macskassy, S.A., Provost, F.: Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research 8, 935–983 (2007) 18. Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings SIGMOD International Conference on Management of Data, pp. 307–318. ACM (1998) 19. Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the International Conference on Machine Learning, pp. 496–503 (2003) 20. Taranto, C., Di Mauro, N., Ferilli, S., Esposito, F.: Approximate image color correlograms. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 1127–1130. ACM (2010) 21. Poole, D.: Abducing through negation as failure: stable models within the independent choice logic. J. Log. Program. 44, 5–35 (2000) 22. Kameya, Y., Ueda, N., Sato, T.: A Graphical Method for Parameter Learning of Symbolic-Statistical Models. In: Arikawa, S., Nakata, I. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 264–276. Springer, Heidelberg (1999) 23. Costa, V.S., Page, D., Qazi, M., Cussens, J.: CLP(BN): Constraint logic programming for probabilistic knowledge. In: Proceedings of the Nineteenth International Conference on Uncertainty in AI, pp. 517–524 (2003) 24. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. The MIT Press (2009) 25. Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th International Confernce on Logic Programming, pp. 715–729. MIT Press (1995) 26. Kimmig, A., Santos Costa, V., Rocha, R., Demoen, B., De Raedt, L.: On the Efficient Execution of ProbLog Programs. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 175–189. Springer, Heidelberg (2008) 27. Shterionov, D., Kimmig, A., Mantadelis, T., Janssens, G.: DNF sampling for problog inference. In: International Colloquium on Implementation of Constraint and Logic Programming Systems (2010) 28. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006) 29. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: IEEE Computer Vision and Pattern Recognition, Workshop on Generative-Model Based Vision (2004)

A Keyphrase-Based Paper Recommender System Felice Ferrara, Nirmala Pudota, and Carlo Tasso Department of Mathematics and Computer Science, University of Udine Via delle Scienze, 206 – 33100 Udine, Italy {felice.ferrara,nirmala.pudota,carlo.tasso}@uniud.it Abstract. Current digital libraries suffer from the information overload problem which prevents an effective access to knowledge. This is particularly true for scientific digital libraries where a growing amount of scientific articles can be explored by users with different needs, backgrounds, and interests. Recommender systems can tackle this limitation by filtering resources according to specific user needs. This paper introduces a content-based recommendation approach for enhancing the access to scientific digital libraries where a keyphrase extraction module is used to produce a rich description of both content of papers and user interests. Keywords: Recommender systems, content-based, keyphrase extraction, adaptive, personalization.

1

Introduction

Information access involves three main actors: the users (who are interested in obtaining knowledge), the document collections (which contain available information), and the access functions (which support the user in extracting knowledge from available resources) [1]. Web growth and evolution have changed the characteristics of both users and document collections. In fact, the participative Web allows a growing number of users to access and populate document collections in a simple way, producing larger and larger collections. As a result, document collections can be explored by a very large set of users, who access the repositories in order to satisfy various personal information needs. Unfortunately, this growing size of the digital information space prevents an effective access to knowledge due to the well-known phenomenon of information overload. Therefore, innovative ways to access Web contents are required. A viable solution to these problems is constituted by adaptive personalization, i.e. to first identify and model the specific information needs of the user (building in such a way a user profile) and to subsequently filter Web resources according to the individual user profile. Also scientific digital libraries (such as CiteULike1 , Elsevier2 and PubMed3 ) show the above criticalities, and 1 2 3

http://www.citeulike.org/ http://www.elsevier.com/ http://www.ncbi.nlm.nih.gov/pubmed

M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 14–25, 2011. c Springer-Verlag Berlin Heidelberg 2011 

A Keyphrase-Based Paper Recommender System

15

to avoid them, they have experimented various innovative mechanisms, such as email alert systems, RSS feeds, and recommender systems. The goal of recommender systems is to model user interests and to filter resources according to the identified user needs and interests. The aim of this paper is to propose a content-based recommender approach for scientific digital libraries, which extracts keyphrases from papers in order to have a rich description of both resources and user interests. A keyphrase is a short phrase (typically constituted by one to three words) that provides a key idea of a document. A keyphrase list is a short list of keyphrases that reflects the content of a single document, capturing the main topics discussed and providing a brief summary of its content. In this work, the keyphrase lists extracted from papers which are relevant to a specific user are exploited to create his/her user profile. Then, in order to compute the relevance of a new article, the user profile is compared with the keyphrase list extracted from that article. This paper is organized as follows: Section 2 introduces related works, focusing specifically on the keyphrase extraction task and on recommender systems for digital libraries. The domain-independent keyphrase extraction technique used to model the content of a resource and to produce the user profile is described in detail in Section 3, as well as the proposed recommendation approach. The evaluation is provided in Section 4, while future work and final considerations conclude the paper in Section 5.

2

Related Work

This section provides the reader with background concepts of keyphrase extraction (2.1) and recommender systems (2.2). 2.1

Keyphrase Extraction

Keyphrase extraction methods have been used successfully in Information Retrieval (IR) and Natural Language Processing (NLP) tasks, such as document indexing [2], classification [3], and automatic tagging [4]. Keyphrase extraction methods usually work in two stages: (i) a candidate identification stage, identifies all possible phrases from the document and (ii) a selection stage selects only few candidate phrases as keyphrases. Existing methods for keyphrase extraction can be divided into supervised and unsupervised approaches. A supervised approach builds a model by using training documents that have already keyphrases previously assigned to them by humans. This model is trained to learn features of the relevant keyphrases (the keyphrases assigned by humans to the training documents) and then it is exploited in order to select keyphrases from unseen documents. KEA [5] is a notable supervised approach which uses a Bayesian classifier. KEA analyzes training documents by taking into account orthographic boundaries (such as punctuation marks, newlines, etc.) in order to find candidate phrases. In KEA two specific features are exploited as metrics in

16

F. Ferrara, N. Pudota, and C. Tasso

order to rank candidate keyphrases: tf×idf (term frequency × inverse document frequency) and the position of the first occurrence of the keyphrase. Hulth [6] introduces linguistic knowledge (i.e., POS, Part-Of-Speech tags) in determining candidate sets: 56 potential Pos-patterns are used for identifying candidate phrases in the text. The experimentation carried out by Hulth has shown that, using a POS tag as a feature in candidate selection, a significant improvement of the keyphrase extraction results can be achieved. Another system that relies on linguistic features is LAKE (Learning Algorithm for Keyphrase Extraction) [7]: it exploits linguistic knowledge for candidate identification and it applies a Na¨ıve Bayes classifier in the final keyphrase selection. All the above systems need training data (in a larger or smaller extent) in order to construct an extraction system. However, acquiring training data with known (i.e., already assigned) keyphrases is not always feasible and human assignment is time-consuming. Furthermore, a model that is trained on a specific domain, does not always produce adequate classification results in other domains. The unsupervised approach eliminates the need of training data. It selects a general set of candidate phrases from the given document, and it uses some ranking strategy to select the most important candidates as keyphrases for the document. Barker and Cornacchia [8] extract noun phrases from a document and ranks them by using simple heuristics, based on their length, frequency, and the frequency of their head noun. In [9], Bracewell et al. extract noun phrases from a document, and then cluster the terms which share the same noun term. The clusters are ranked based on term and noun phrase frequencies. Finally, the topn ranked clusters are selected as keyphrases for the document. The authors of [10] and [11] proposed unsupervised approaches based on a graph representation of documents. Such approaches use ranking strategies (similar to the PageRank algorithm [12]) to assign scores to each term. Keyphrase extraction systems that are developed by following unsupervised approaches are in general domain independent since they are not constrained by specific training documents. 2.2

Recommender Systems

Information overload is the main motivation for recommender systems: they support users during their interaction with large information spaces, directing them toward the specific information they need [13]. Recommender systems filter relevant content according to individual information needs of a specific user (in this paper, referred also as active user ). In order to reach their aim, recommender systems can possibly exploit a suitable representation of user interests, goals, knowledge, and tastes, by monitoring and modeling implicit and/or explicit feedback provided by the user. By analyzing such user feedback, a recommender system is capable of computing a personalized rank for the set of available resources. The most common classification of recommender systems takes into account the algorithm used to produce recommendations and identifies three classes of recommender systems: collaborative filtering, content-based, and hybrid recommender systems [14]. Collaborative filtering recommender systems filter resources

A Keyphrase-Based Paper Recommender System

17

by using opinions provided by other people. Content-based recommender systems analyze the past user activities looking for resources she liked; they model resources by extracting some features (for example, topics or relevant concepts) from documents. The user profile is then built by identifying features which are interesting for the user. The relevance of a new resource for a user is then computed by comparing the representation of the resource to the user profile. Hybrid recommender systems variously combine results returned by collaborative and content-based recommender systems [15]. Recommender systems have been widely proposed to face information overload in digital libraries. The authors of [16] propose a collaborative filtering recommender system aimed at taking into account that each user of a digital library may be interested in several distinct topics. Their work focuses on innovative digital libraries which include Web 2.0 features such as social tagging: the active participation of Web 2.0 users is exploited in order to identify different topics of interest (ToI) of the users. This is achieved by clustering the tags utilized by a user, joining together tags with similar meanings. The similarity depends on the number of times two tags have been applied on the same resource. Such tag clusters allow to split resources tagged by the user into different collections, each one associated to a specific ToI. Only opinions of users interested into a specific ToI are then considered to compute recommendations. In particular, resources labeled by tags which are evaluated as more similar to the tags associated to a ToI are considered more relevant than other resources, and resources bookmarked by users more similar to the active user are more relevant than others as well. A content-based approach has been proposed in [17] where authors and papers are modeled by trees of concepts: using the ACM Computing Classification System (CCS), the authors trained a vector space classifier in order to associate concepts of the CCS classifications to documents. The hierarchical organization of the CCS allows the system to represent user interests and documents by trees of concepts. A user profile and a paper representation are then compared by a tree edit-distance which computes a similarity measure among trees. The authors of [18] proposed a paper recommender system which takes into account authors’ publications and the papers they cite in order to define a user profile and then compute recommendations. Term frequency is used to create a weighted vector of terms to model user interests: the textual content of both the papers written by the active user and papers cited by the active user are considered for building his profile. Similarly, resources are represented as vectors using the tf×idf metric. Then, the relevance of a document with respect to the active user depends on the cosine similarity among the user profile and the resource representation. PaperRank [19] is a PageRank-like method [12] to filter relevant papers by exploring the citation graph according to a seed of input documents. The approach is a modified version of the PageRank algorithm which gives higher relevance to the papers relevant for the active user.

18

3

F. Ferrara, N. Pudota, and C. Tasso

The Proposed Approach

This section presents the proposed approach, whose general organization is shown in Figure 1. Our proposal is included in the Pirates project [20] aimed

Fig. 1. General organization of the proposed approach

at studying and developing new advanced tools for Web information access, classification, retrieval, and extraction. One of the goals of the overall Pirates framework is to recommend, by means of various techniques, new relevant content (as well as classification information, such as tags [16][21]) in an adaptive personalized way. The part of the Pirates framework that we are presenting in this paper takes into account that a user has normally associated to him a set of documents of interest, which have been usually (but not necessarily nor exclusively) identified by tagging. So if a user has a set of tagged documents, these can be exploited by the KPEM (Key Phrase Extraction Module) for building his user profile. On the other hand, if a new document is extracted from the Web (for example by the IFT [22] content based filtering module present in Pirates), the same processing exploiting KPEM can be utilized for building a representation of the document. The Matching Module takes finally both the user profile and the document representation in order to compute a matching score, and to identify documents to be suggested to the user. 3.1

Extracting Keyphrases

Given a paper p, the unsupervised keyphrase extraction approach used in our proposal exploits the following three main steps: (i) extract candidate phrases from p (ii) calculate feature values for candidates (iii) compute a score for each candidate phrase from its feature values and filter the top weighted keyphrases. The following subsections illustrate the three steps.

A Keyphrase-Based Paper Recommender System

19

Step1: Candidate Phrase Extraction. In this phase three main steps are exploited: – POS tagging and n-gram extraction. We assign a POS tag (noun, adjective, verb, etc.) to each token in the text (for English documents we use the Stanford log-linear part-of-speech tagger4 while, for documents written in Italian, we developed ad-hoc an Italian POS tagger). Subsequently, ngrams are extracted and since keyphrases constituted by more than 3 words are usually very rare, we extract only all possible subsequences of phrases up to 3 words (uni-grams, bi-grams, and tri-grams). – Stemming and stopword removing . From the extracted n-grams, we remove all phrases5 that start and/or end with a stopword. Partial stemming (i.e., unifying the plural forms and singular forms which refer to the same meaning) is performed using the first step of Porter stemmer algorithm [23]. To reduce the size of the candidate phrase set, we filter out some of them by means of a rule-based engine which uses POS tagging information: for example, uni-grams that are not labeled as noun, adjective, or verb are filtered, bi-grams and tri-grams are filtered according to [24], and so on. – Separating n-gram lists. Generally, in a document, uni-grams are more frequent than bi-grams, and bi-grams are more frequent than tri-grams. This introduces an undesidered bias in the subsequent computation of phrase features. In order to avoid this, we separate n-grams of different lengths and arrange them in three different lists, then treated separately. Step2: Feature Computation. This step characterizes each candidate phrase (included in the three n-gram lists) by statistical and linguistic properties. The following five features are computed for each candidate phrase: – Phrase Frequency . It is the classical term frequency (tf) metric. But instead of calculating it with respect to the whole length of the document, we compute it with respect to each n-gram list. With reference to each n-gram list, the phrase frequency for phrase P in list L is: f requency(P, L) =

f req(P, L) , size(L)

where f req(P, L) is the number of times P occurs in L and size(L) is the total number of phrases included in L. – POS Value. As described in [8], most author-assigned keyphrases for a document turn out to be noun phrases. For this reason, in our approach, we stress the presence of a noun in a candidate phrase while computing a POS Value for the phrase. A POS Value is assigned to each phrase by calculating the number of nouns (singular or plural) normalizing it by the total number of terms in the phrase. All remaining phrases which do not include at least one noun term are assigned a lower POS Value. 4 5

http://nlp.stanford.edu/software/tagger.shtml. In our use of this term, we mean any n-gram (n=1,2,3) phrase.

20

F. Ferrara, N. Pudota, and C. Tasso

– Phrase Depth. This feature reflects the assumption/belief that important phrases often appear in the initial part of the document especially in news, articles, and scientific publications (e.g., abstract, introduction). We compute the position in the document where the phrase first appears. The Phrase Depth value for phrase P in a document D is: depth(P, D) = 1 − [

f irst index(P ) ], size(D)

where f irst index(P ) is the number of words preceding the phrase’s first appearance and size(D) is the total number of words in D. The result is a number between 0 and 1. Highest values represent the presence of a phrase at the very beginning of the document. – Phrase Last Occurrence. We give also importance to phrases that appear at the end of the document, as it is the case in scientific articles (i.e., in conclusion and discussion parts). The last occurrence value of a phrase is calculated as the number of words preceding the last occurrence of the phrase normalized to the total number of words in the document. The last occurrence value for phrase P in a document D is: last occurrence(P, D) =

last index(P ) , size(D)

where last index(P ) is the number of words preceding the phrase’s last appearance and size(D) is the total number of words in D. – Phrase Lifespan: the span value of a phrase depends on the portion of the text that is covered by the phrase. The covered portion of the text is the distance between the first occurrence position and last occurrence position of the phrase in the document. The lifespan value is computed by calculating the difference between the Phrase Last Occurrence and the phrase first occurrence. The lifespan value for phrase P in a document D is: lif espan(P, D) =

[last index(P ) − f irst index(P )] , size(D)

where last index(P ) is the number of words preceding the phrase’s last appearance and f irst index(P ) is the number of words preceding the phrase’s first appearance; size(D) is the total number of words in D. The result is a number between 0 and 1. As a result of step 2, we get a feature vector for each candidate phrase in the three n-gram lists. Step3: Scoring and Ranking. In this step a score is assigned to each candidate phrase which is later exploited for the selection of the most appropriate phrases as representatives of the content of a document. The score of each candidate phrase is calculated as a linear combination of the 5 above features. We call

A Keyphrase-Based Paper Recommender System

21

the resulting value the keyphraseness of the candidate phrase. The keyphraseness of a phrase P with a non empty feature set {f1 ,f2 ,...,f5 }, with non-negative weights {w1 ,w2 ,..,w5 } is: 5 wi fi keyphraseness(P ) = i=1 5 i=1 wi In the initial stage of the research, we have assigned equal weights to all features, yielding to the computation of the average. Therefore: 1 fi , n i=1 n

keyphraseness(P ) =

where n is the total number of features, f1 is the Phrase Frequency, f2 is the Phrase Depth, f3 is the phrase POS Value, f4 is the Phrase Last Occurrence, and f5 is the Phrase Lifespan. Producing Final Keyphrases. The scoring process produces the three ranked lists U niGrams(p), BiGrams(p), and T riGrams(p), each one containing respectively all the uni-grams, bi-grams, and tri-grams extracted from the paper p with their keyphraseness values. In order to filter only the most relevant keyphrases, the three output lists are pruned by removing keyphrases with a keyphraseness lower than a fixed threshold. 3.2

Computing Recommendations

Given a user, the papers that he tagged are considered relevant papers for building the User Profile. The profile is constituted by three (ordered) lists of weighted and stemmed keyphrases: the list of uni-grams (the uni-gram profile), the list of bi-grams (the bi-gram profile), and the list of tri-grams (the tri-gram profile). More specifically, given the list of relevant papers {p1 , . . . , pn } for the active user, we exploit the keyphrase extraction approach described above to extract uni-grams, bi-grams, and tri-grams from each relevant paper separately. This step produces three lists of weighted keyphrases: 1. the list of the weighted uni-grams {U niGrams(p1 ), . . . , U niGrams(pn )}; 2. the list of the weighted bi-grams {BiGrams(p1 ), . . . , BiGrams(pn )}; 3. the list of the weighted tri-grams {T riGrams(p1 ), . . . , T riGrams(pn )}. All uni-grams lists {U niGrams(p1 ), . . . , U niGrams(pn )} are then merged to build the uni-gram profile and, similarly, the lists of bi-grams and tri-grams are merged to build the bi-gram and the tri-gram profiles. More specifically, given the lists of uni-grams extracted from the relevant papers, each distinct uni-gram is stemmed and then inserted in the final list of relevant keyphrases. The weight assigned to each uni-gram in the uni-gram profile is computed by summing the weights associated to it by the keyphrase extraction technique. The weight of each keyphrase is then multiplied by the idf value associated to the specific keyphrase. The same technique is applied to produce the bi-gram profile and

22

F. Ferrara, N. Pudota, and C. Tasso

the tri-gram profile. Figure 2 shows the most relevant uni-grams, bi-grams and tri-grams extracted from the user profile of one of the users (using a set of 10 relevant documents and removing the uni-grams with a keyphraseness lower the 0.9, the bi-grams with a keyphraseness lower than 0.8 and the tri-grams with a keyphraseness lower than 0.8) in the dataset described in the next section.

Fig. 2. An example of a user profile. The most relevant n-grams.

In order to compute the relevance of a new paper pk for a given user profile, the approach follows a similar path: it extracts the three lists of keyphrases from the paper and, then, these keyphrases are stemmed. The final step is performed by the Matching Module (see Figure 1), which takes in input three lists, U niGrams(pk ), BiGrams(pk ) and T riGrams(pk ), and the user profile. The matching process is based on the cosine similarity producing three similarity values, one for each category of n-grams. Then, an appropriate combination (linear in the first experiments) of these three similarity values is used to compute a unique score to be assigned to the considered paper pk . Finally, they highest score papers are recommended to the active user.

4

Evaluation

The proposed approach extracts keyphrases from scientific papers in order to have a rich description of user interests which, in turn, is exploited to improve the quality of a content-based recommender system. The main assumption is that keyphrases have meaningful contextual information (not accounted in the classical bag-of-word model) which can be used to improve a cognitive filtering mechanism. In order to validate our claim we performed some experimental evaluation by using a publicly available dataset which contains 597 full papers extracted from the ACL Anthology Reference Corpus (ACL ARC)6 : this dataset has been built from a significant subset of the ACL Anthology, a scientific digital library of 6

http://acl-arc.comp.nus.edu.sg/

A Keyphrase-Based Paper Recommender System

23

papers on natural language processing and computational linguistics, composed by 10921 papers published since February 2007. The dataset includes specific data about 28 researchers (15 junior researchers and 13 senior researchers), interested in natural language processing. In particular, each researcher reported his relevant papers. In our evaluation we used this feedback to both build the user profiles of researchers and evaluate the precision of the computed recommendations. More specifically, given a researcher we divided the set of his preferences into two set of papers. We used one of these sets (constituted by 20 papers) as training set in order to build the user profiles while the second set has been used as test set for comparing the results provided by the recommendation engine. According to this setting we computed the recommendations for the researchers in the dataset. Moreover, in order to evaluate the improvement with respect to a baseline bagof-word approach, we also built the user profiles and document representations using only uni-grams. By using this setting (which does not include bi-grams and tri-grams) we computed a new set of recommendations to be used as a baseline reference for performance. The following table compares the precision of the recommendations obtained by using only unigrams to the precision obtained by using the approach described in 3.2 where the precision is computed as the ratio among the number of correct recommendations and the number of produced recommendations. Table 1. The precision of the proposed approach vs. the precision obtained by using only uni-grams Uni-grams Based The Proposed Approach p@1 0.83 0.93 p@3 0.61 0.77 p@5 0.65 0.80 p@7 0.64 0.73

The table shows that by tanking into account also bi-grams and tri-grams it is possible to obtain an higher precision.

5

Conclusion and Future Work

In this work we introduced a content-based paper recommender system which produces rich user profiles and resource descriptions by extracting keyphrases from scientific articles. The system is intended to work in combination with other modules of an hosting framework, currently under development within Pirates, a larger project aimed at innovate within a social/semantic approach the tools for access, classification, filtering, retrieval, and extraction of Web information. The proposed approach is based on adaptive user profiles and semantic descriptions of resources (documents), which are compared by the cosine similarity to evaluate the relevance of a new document with respect to the user

24

F. Ferrara, N. Pudota, and C. Tasso

interests. At the moment, a more extensive evaluation aimed at comparing the proposed approach with other techniques is ongoing. Other future activities will focus on integrating this basic approach with the ontology mining module of the Pirates framework in order to improve the description of both user interests and resources by a deeper semantic representation. Finally, we are currently designing both content-based and collaborative recommender systems able to merge collaborative knowledge (provided from Web 2.0 users) to the semantic knowledge extracted by the Pirates framework.

References 1. Agosti, M.: Information Access Through Digital Library Systems. In: Goh, D.H.L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 11–12. Springer, Heidelberg (2007) 2. Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domain-specific keyphrase extraction. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, July 31-August 6, pp. 668–673. Morgan Kaufmann Publishers, San Francisco (1999) 3. Krulwich, B., Burkey, C.: Learning user information interests through the extraction of semantically significant phrases. In: Hearst, M., Hirsh, H. (eds.) AAAI 1996 Spring Symposium on Machine Learning in Information Access, pp. 110–112. AAAI Press, California (1996) 4. Pudota, N., Dattolo, A., Baruzzo, A., Ferrara, F., Tasso, C.: Automatic keyphrase extraction and ontology mining for content-based tag recommendation. International Journal of Intelligent Systems, Special Issue: New Trends for Ontology-Based Knowledge Discovery 25, 1158–1186 (2010) 5. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: Kea: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255. ACM, New York (1999) 6. Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 216–223. Association for Computational Linguistics, Morristown (2003) 7. D’Avanzo, E., Magnini, B., Vallin, A.: Keyphrase extraction for summarization purposes: the lake system at duc2004. In: DUC Workshop, Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting, Boston, USA (2004) 8. Barker, K., Cornacchia, N.: Using Noun Phrase Heads to Extract Document Keyphrases. In: Hamilton, H.J. (ed.) Canadian AI 2000. LNCS (LNAI), vol. 1822, pp. 40–52. Springer, Heidelberg (2000) 9. Bracewell, D.B., Ren, F., Kuroiwa, S.: Multilingual single document keyword extraction for information retrieval. In: Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, Wuhan, pp. 517–522 (2005) 10. Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. In: Dekang, L., Dekai, W. (eds.) Proc. of Empirical Methods in Natural Language Processing, pp. 404–411. Association for Computational Linguistics, Barcelona (2004)

A Keyphrase-Based Paper Recommender System

25

11. Litvak, M., Last, M.: Graph-based keyword extraction for single-document summarization. In: Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, pp. 17–24. ACL, Morristown (2008) 12. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks 30, 107–117 (1998) 13. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transaction on Knowledge and Data Engineering 17, 734–749 (2005) 14. Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A., Cohen, M.D.: Intelligent information-sharing systems. Communications of ACM 30, 390–402 (1987) 15. Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007) 16. Dattolo, A., Ferrara, F., Tasso, C.: Supporting Personalized User Concept Spaces and Recommendations for a Publication Sharing System. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 325–330. Springer, Heidelberg (2009) 17. Chandrasekaran, K., Gauch, S., Lakkaraju, P., Luong, H.P.: Concept-Based Document Recommendations for CiteSeer Authors. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 83–92. Springer, Heidelberg (2008) 18. Sugiyama, K., Kan, M.-Y.: Scholarly paper recommendation via user’s recent research interests. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, JCDL 2010, pp. 29–38. ACM, New York (2010) 19. Gori, M., Pucci, A.: Research paper recommender systems: A random-walk based approach. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2006, pp. 778–781. IEEE Computer Society, Washington, DC (2006) 20. Baruzzo, A., Dattolo, A., Pudota, N., Tasso, C.: A General Framework for Personalized Text Classification and Annotation. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 31–39. Springer, Heidelberg (2009) 21. Dattolo, A., Ferrara, F., Tasso, C.: The Role of Tags for Recommendation: a Survey. In: Hippe, Z., Kulikowski, J., Mroczek, T. (eds.) Backgrounds and Applications 2. AISC. Springer, Heidelberg (in printing) 22. Tasso, C., Asnicar, F.A.: ifweb: a prototype of user model-based intelligent agent for document filtering and navigation in the world wide web. In: 6th UM Inter. Conf. Adaptive Systems and User Modeling on the WWW (1997) 23. Porter, M.F.: An algorithm for suffix stripping. Readings in Information Retrieval, 313–316 (1997) 24. Justeson, J., Katz, S.: Technical terminology: some linguistic properties and an algorithm for identification in text. Natural Language Engineering 1, 9–27 (1995)

Accessing Music Digital Libraries by Combining Semantic Tags and Audio Content Riccardo Miotto and Nicola Orio Department of Information Engineering, University of Padua Via Gradenigo, 6/a – 35131 Padua, Italy {miottori,orio}@unipd.it

Abstract. An interesting problem in accessing music digital libraries is how to combine the information of different sources in order to improve the retrieval effectiveness. This paper introduces an approach to represent a collection of tagged songs through an hidden Markov model with the purpose to develop a system that merges in the same framework both acoustic similarity and semantic descriptions. The former provides content-based information on song similarity, the latter provides contextaware information about individual songs. Experimental results show how the proposed model leads to better performances than approaches that rank songs using both a single information source and a their linear combination.

1

Introduction

The widespread diffusion of digital music occurred during the last years has brought music information retrieval (MIR) to the general attention. A central goal of MIR is to create systems that can efficiently and effectively retrieve songs from collections of music content (e.g. music digital libraries) according to some sense of similarity with a given query. In information retrieval systems, the concept of similarity plays a key role and can dramatically impact performances. Yet, in music applications, the problem of selecting an optimal similarity measure is even more difficult because of the intrinsic subjectivity of the task: users may not consistently agree upon whether, or at which degree, a pair of songs or artists are similar. In the last years, in order to deal with the subjective nature of music similarity, it became very common to describe songs as a collection of meaningful terms, or tags, as done in Last.fm1 and Pandora2. In particular, tags are often, directly or indirectly, provided by end users and can represent a variety of different concepts including genre, instrumentation, emotions, geographic origins, and so on. Many approaches have been developed to collect tags, ranging from mining the Web and exploiting social behavior of users, to automatic annotation of music through machine learning algorithms. Tags are useful because they contextualize 1 2

http://www.last.fm http://www.pandora.com

M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 26–37, 2011. c Springer-Verlag Berlin Heidelberg 2011 

Accessing Music Digital Libraries by Combining Semantic Tags

27

a song – for instance describing an historical period, a geographical area, or a particular use of the song – through an easy high-level representation. This information can then be used to retrieve music documents, to provide recommendations or to generate playlists. Excluding the case of Pandora, where songs are annotated by human experts to guarantee high quality and consistency, in automatic systems or when the social behavior of users is kept into account, the semantic descriptions may be very noisy. In automatic approaches, for example, the quality of the prediction strictly depends on the quality of the training set, on the quality of the model, and on other issues such as parameter overfitting or term normalization. On the other hand, standard content-based music similarity, computed directly on music features, can be exploited to improve the quality of the retrieval, without requiring additional training operations. The goal of this paper is to provide a general model to describe a music collection and easily retrieve songs combining both content-based similarity and context-aware tag descriptions. The model is based on an application of hidden Markov models (HMMs) and of the Viterbi algorithm to retrieve music documents. The main applicative scenario is cross-domain music retrieval, where music and text information sources are merged. 1.1

Related Work

There has been a considerable amount of research devoted to the topic of music retrieval, recommender systems and music similarity. Some of the most wellknown commercial and academic systems have been described in [1]. The model proposed in this paper fits the scenario of item-based retrieval systems, combining pure acoustic similarity and semantic descriptions. Methodologies that merge different heterogeneous sources of information have been recently proposed for the task of semantic discovery [2], artist recommendation [3] and music classification [4]. All of these approaches learn a metric space to join and compare the different sources of information in order to provide the user with a single ranking list. Our approach is consistently different, because it is built on a graph-based representation of the collection that model both sources of information and thus it does not rely on an additional processing to combine them. Content-based music similarity can be computed directly on music features [5, 6] or through a semantic space which describes music content with meaningful words [7, 8]. In our work, we exploit the properties of an HMM to combine these two descriptions to improve retrieval performances. As it is well known, HMMs have been extensively used in many applications, which in particular involve processes through time such as speech recognition [9]. In the music information retrieval research area, they have been used in different scenarios: query-by-example [10], automatic identification [11], alignment [12], segmentation [13], and chord recognition [14]. At the best of our knowledge, this is the first application of HMMs in the task of cross-domain retrieval where music and text information is modeled in a single framework.

28

2

R. Miotto and N. Orio

Statistical Modeling of a Music Collection

The general goal of accessing to music digital libraries is to retrieve a list of songs according to a particular principle. The principle could be described either directly by a general semantic indication, such as the tag “classic rock”, or indirectly by a song, such as the set of tags assigned to “Yesterday, The Beatles”. In both cases, the principle represents a user information need, and it can be assumed that the goal of an user is to observe consistently the application of this principle during the time of his access to the music collection. In the particular case of playlist generation, a system should be able to retrieve a list of music documents that are acoustically similar to the music the user likes and, at the same time, are relevant to one or more semantic labels that give a context to his information need. The methodology presented in this paper aims at providing a formal and general model to retrieve music documents combining acoustic similarity and semantic descriptions given by social tags. That is, the goal is to propose a model that encompasses both content-based similarity and context-aware descriptors. To this end, HMMs are particularly suitable because they allow us to model two different sources of information. In fact, HMMs represent a doubly embedded stochastic process where, at each time step, the model performs a transition to a new state according to transition probabilities and emits a new symbol according to observation probabilities. Thus HMMs can represent either content and context information, under the following assumptions: – if each state represents a song in the collection, acoustic content-based similarity can be modeled by transition probabilities – if the symbols emitted by the HMM are semantic labels, the context that describes each state can be modeled by observation probabilities. A suitably built HMM (see Section 2.1) may be exploited to address the examples provided at the beginning of this section. On the one hand, the model can generate a path across songs while observing, for a defined number of time steps, the semantic label “classic rock”. On the other hand, the model can start the path from the state associated to “Yesterday” and proceed to new states while observing the semantic labels associated to the seed song. In both cases, the songs in the path are likely to have a similar content because of transition probabilities and are likely to be in the same context because of emission probabilities. Since states of a HMM are not directly observable, the paths across the song collection need to be computed by a decoding step, which highlights the most probable state sequence according to a sequence of observations. A representation of the proposed model is depicted in Figure 1. 2.1

Definition of the HMM

An HMM λ that represents a collections of tagged songs can be formally defined by:

Accessing Music Digital Libraries by Combining Semantic Tags

29

Sim(s1, s2)


s2


a12
B2(.)


a32


B3(.)


s3


B1(.)
s 1
Semantic Description


Beatles, Yesterday
rock, acoustic guitar,
ballad, sad …


Music Collection
hidden Markov model


Fig. 1. General structure of the model: each song is represented by a state and is described by a set of tags. States are linked together by edges weighted according to acoustic similarity between the songs.

1. The number of songs N in the collection, each song represented by a state of the HMM. The set of states is denoted as S = {s1 , s2 , ..., sN }. 2. The number M of distinct tags that can be used to describe a song. The set of symbols is denoted as V = {v1 , v2 , ..., vM }. 3. The state transition probability distribution A = aij , which defines the probability to move from state i to state j in a single step. Transition probabilities aij depends to the similarity between songs si and sj . 4. The observation probability distribution of each state j, B = bj (k), which defines the probability that tag vk is associated to song j. Observation probability values represent the strength of the relationships song-tag, which is indicated as affinity value. 5. The initial state distribution π = {πi }, that defines the probability to start a path across the model beginning at state si . Differently from the standard definition of HMMs, the initial state distribution is computed dynamically at retrieval time, since it is strictly connected to the type of information need, as described in Section 2.3. Although acoustic similarity is always a positive value, implying aij > 0 ∀i, j, with the aim of improving scalability, each state is directly connected to only the P most similar songs in the collection, while the transition probabilities with all the other states are set to 0. Heuristically, we set P to be the 10% of the global number of songs. At present, no deeper investigation has been carried out to highlight an optimal value of P . In order to obtain a stochastic  model, both transition and emission probabilities are normalized, that is j aij = 1 and  b (k) = 1. Because of these two steps, transition probabilities are usually j k not symmetric, then aij = aji . After setting all the parameters, the HMM can be used to generate random sequences, where observed symbols are tags. Dually, well known algorithms can

30

R. Miotto and N. Orio

be used to decode the most probable state sequence according to a given observation sequence. 2.2

Computing the Relevance of Songs

The task at retrieval time is to highlight a sub-set of songs in the collection that are relevant to a particular query, either expressed by semantic labels or by a seed song. In the context of HMMs, the general problem can be stated as follows [9]: ¯ = {o(1), . . . , o(T )} with “given the model λ, and the observation sequence O ¯ oj ∈ V , the goal is to choose a state sequence S = {s(1), . . . , s(T )} which is optimal in some sense”. Clearly, the observations sequence represents the semantic description specified by the user need. In literature, this problem is solved using the max-sum algorithm, which in HMMs applications is known as the Viterbi algorithm. The algorithm efficiently searches in the space of paths, in order to find the most probable one, with a computational cost that grows only linearly with the length of the chain. The algorithm is composed by a forward computation to find the maximization for the most probable path, and by a backward computation to decode the sequence of states. Although the general structure of the algorithm has been maintained, some key modifications in the recursion part of the forward computation have been introduced. Following the notation and the algorithm description provided in [9] the normal initialization and the modified recursion steps follow, for 1 ≤ j ≤ N: Initialization: for t = 1 δt (j) = πj · obsj (t) ,

ψt (j) = 0 .

(1)

δt (j) = max [δt−1 (i) · aij ] · obsj (t) ,

(2)

ψt (j) = arg max [δt−1 (i) · aij ] ,

(3)

Recursion: for 2 ≤ t ≤ T 1≤i≤N

1≤i≤N

akj =

akj d

with

k = ψt (j) .

(4)

As it can be seen, we introduce obsj (t), defined in the next section, which is a general function that indicates how the semantic description is considered during the retrieval process. This function plays the role of observations in typical decoding applications. Equation 4 introduces a variation of the role of transition probabilities. In fact, because of the structure of the model, it could happen that the optimal path enters a loop between the same subset of songs or, in the worst case, jumps back and forth between two states. Clearly, this is a problem because the

Accessing Music Digital Libraries by Combining Semantic Tags

31

retrieved list would present the same set of songs multiple times. Moreover, the loop could be infinite, meaning that the algorithm cannot exit from it and the retrieval list would be composed by only few songs. We addressed this problem by introducing a decreasing factor d, which is applied to the transitions probabilities when they are selected in the forward step. So, when a transition is chosen, the probability aij is decreased by factor d (we set d = 10), as shown in Equation 4, in order to make unlikely that the state sequence would pass again through the corresponding edge. It has to be noted that the attenuation is carried out locally, meaning that it affects the structure of the model only during the current retrieval operation. Another issue that has to be addressed is a limitation in the structure of standard HMMs. Because of the first-order Markov chain assumption, HMMs are generally poor at capturing long-range correlations between the observed variables, that is between variables that are separated by many steps [15]. Earlier experiments showed that this limitation involved a decrease in precision when decoding long paths. In order to solve this problem, we considered the retrieval composed by many sub-retrieval operations, each one retrieving a sub-list of songs. Instead of performing a single backward decoding, the algorithm works for a subset of iterations, from which an optimal sub-path is built. Only the first n songs of this sub-path are considered in the final ranking list; at the end of each iteration the algorithm restarts from the last state of the n suggested. Given the locality of the approach, in this way we aim to keep constant the quality along the retrieved list, avoiding a decrease in precision. 2.3

Querying the Model

As often assumed in the interaction with music search engines, in our scenario a user can submit a query in two distinct ways: by providing a tag or by selecting a seed song in the collection. According to the kind of query, some of the model parameters are set differently. In the tag-based scenario, the goal is to rank the songs according to their relevance with the provided tag and, at the same time, to their acoustic similarity. In this case, the observation sequence is composed simply by the chosen tag. We decided to set the initial state probability equal for all the states, in order to let the algorithm decide the beginning of the retrieved list. This scenario is very related to the standard HMMs case, then the function obsj (t) of Equations 1 and 2 is defined as obsj (t) = bj (ot )

(5)

for a generic state j, where observations ot may be the same tag for all the time steps or it may change over time in case of playlist generation through more complex patterns. In the seed-song scenario, when the query is submitted as a song q, the system is required to provide the user with a list of songs potentially similar to the query. In this case, the initial state distribution is forced to be 1 for the state

32

R. Miotto and N. Orio

representing the seed song and 0 for all the others. The observation sequence to be decoded is modeled as the vector of observations characterizing the seed song. The function obsj (t) of Equations 1 and 2 is proportional to the inverse of the Kullback-Leibler (KL) divergence between the semantic description of the seed song and the chosen state [16]. The choice of the KL divergence aims at generalizing the terms used for the tags, because it is related to the similarity of concepts associated to the tags rather than to the pure distance between lists of tags. It is important to note that the KL divergence is required also because each song is described by a set of tags. Clearly, we consider the inverse because the goal is to maximize the probability when the divergence is small. Therefore, obsj (t) =

1 , where KL(bj (·), bq (·))

KL(bj (·)  bq (·)) =

M  i=1

bj (i) · log

bj (i) , bq (i) (6)

for the generic state j and the initial seed state q; clearly, observations of q do not change over time t being linked to observations of the seed song. Since it is an observation probability, the actual value of obsj (t) undergoes a normalization process. It is worth noting that the use of KL divergence can be extended also to the tag-based scenario when the user provides a set of tags (instead of a single one) although this extension has not been tested yet.

3

Experimental Evaluation

A big challenge when designing a music retrieval system is how to evaluate a novel methodology. Although several efforts have been made within the MIREX campaigns, because of well-known copyright issues, data of past campaigns are not always available to test new approaches. Ideally, the list of retrieved songs should be evaluated by humans, in order to consider effectively the subjective nature of the concept of music similarity. Being human evaluation a time consuming task, we use an automatic approach considering that reliable annotations on songs can be exploited to measure the quality of a ranking list. We tested our model through the Computer Audition Lab (CAL500) [17] dataset: 502 songs played by 502 unique artists, each one annotated by a minimum of 3 individuals using a vocabulary of 174 tags. A song is considered to be annotated with a tag if 80% of the human annotators agreed that the tag would be relevant. CAL500 is a reasonable ground truth because annotations are highly reliable, complete and redundant – i.e. multiple persons explicitly evaluated the relevance of every tag for each song. So far, it has been mainly used to evaluate automatic music annotation systems, but we believe that it could be a reasonable ground truth also to evaluate qualitatively a retrieval task. Although the size of the dataset does not allow to perform experiments in terms of scalability, we argue that, at this point, it is more significant to test the effectiveness of the approach, to show if the model can provide improvements in the retrieval process.

Accessing Music Digital Libraries by Combining Semantic Tags

33

In the experiments reported in this section, we require that each tag is associated with at least 30 songs and remove some tags that seemed to be redundant or overly subjective. The semantic space is then composed by 62 tags describing information about: genre, instrument, acoustic qualities, vocal characteristic, emotion, and usage. Retrieval is evaluated with metrics considering both performances at the top and along the whole ranking list. Since a music retrieval system should maximize the quality of the retrieved items in the first positions, we evaluate the precision at the first 3, 5 and 10 positions (P3, P5, P10). Beside, we include the mean average precision (MAP) measure, in order to have also an evaluation along the whole ranking list. All these metrics are extensively used in the literature to assess the effectiveness of a retrieval system [18]. 3.1

Acoustic Content-Based Similarity

A number of methodologies have been proposed in literature to compute direct acoustic content-based similarity. In this set of experiments, we rely on the algorithm proposed by Mandel and Ellis [5], which uses a single Gaussian with full covariance to model a song. Although, some alternative approaches have been recently proposed [6], we use this one because of its efficiency and simplicity in the implementation. Songs are represented through vectors of Mel-Frequency Cepstral Coefficients together with their first and second derivatives (MFCC + delta) extracted from about one minute of music content, and the similarity between songs is computed using a symmetrized version of the KL divergence. Section 2.1 describes how transition probabilities are computed from these similarity values, in particular by selecting for each  state si the first P most similar songs and performing the normalization j aij = 1 with sj ∈ P . It is important to note that we aim at proposing a general approach, which is independent on the way acoustic similarity is actually computed and which can be applied to other audio descriptors and other similarity measures. For this reason the computation of acoustic similarity is presented within the experimental evaluation section. 3.2

Semantic Space

There are several approaches to collect tags for music, each with its own advantages and disadvantages [19]. Among all, we chose two different representations. A first semantic description has been computed from the music content. We used the Gaussian mixture model described by Turnbull et al. [7] to automatically annotate songs with tags based on audio content analysis. For a given song, the output of this algorithm is a vector of posterior probabilities named semantic multinomial that represents the strength of the relationship tag-song for each tag in the vocabulary. We refer to this description as “cb-auto-tags”. A second representation has been created by gathering the social tags from Last.FM, as reported on February 2010. For each song of the dataset, we collected

34

R. Miotto and N. Orio Table 1. The retrieval results using 62 distinct tags as queries

Semantic

Model

P3

P5

P10 MAP

Random 0.165 0.171 0.166 0.141 cb-auto-tags

HMM 0.516 0.488 0.452 0.361 TAG

Last.fm

0.419 0.431 0.405 0.332

HMM 0.347 0.331 0.268 0.225 TAG

0.303 0.297 0.218 0.207

two lists of social tags using their public data sharing AudioScrobbler3 website. We gathered both the list of tags related to a song, and the list of tags related to an artist. The relevance score between a song and a tag is given by the sum of the scores in both lists, plus the tag score for any synonym or other wild matches of the tag in both lists [2]. Social tag scores are then mapped to the equivalent class in our semantic description. If no gathered tag for a given song belonged to the semantic space, the semantic description is represented by a uniform distribution, where all the tags share the same score. This lead to a very sparse and noisy description, which is useful to test the effectiveness of our approach. We refer to these tags as “Last.fm”. We addressed these descriptions with two different evaluations, although they could be combined together in a single richer semantic description [2]. 3.3

Tag-Based Retrieval

In this first experiment, the model is queried using a tag; a semantic concept is provided to the system, and the goal is to rank all the songs according to their relationships with that term. Metrics are then averaged through all the terms in the vocabulary. Retrieval performances are measured by finding the positions, along the ranking list, of the documents annotated with the considered tag in the ground truth. HMM-based retrieval is compared with the retrieval performed by simply ranking the songs according to their affinity value for that tag (TAG), as well as with a random baseline. Results are reported in Table 1, considering both types of semantic description. As it can be seen, HMM-based retrieval clearly outperforms the retrieval based on a single tag, with a major improvement in the quality at the top of the ranking list. On the other hand, retrieval along the full list tends to decrease its effectiveness, as it can be inferred by the lower improvement achieved by MAP. This is probably due to the problem, discussed in Section 2.2, of HMMs generally 3

http://ws.audioscrobbler.com/2.0/

Accessing Music Digital Libraries by Combining Semantic Tags

35

Table 2. The retrieval results using 50 random seed songs as queries Semantic

Model

P3

P5

P10 MAP

Random 0.113 0.104 0.096 0.050

cb-auto-tags

TAG

0.266 0.270 0.246 0.211

AB

0.237 0.234 0.236 0.187

WLC

0.280 0.278 0.244 0.204

HMM 0.295 0.288 0.258 0.225

Last.fm

Tag

0.273 0.272 0.262 0.191

AB

0.237 0.234 0.236 0.187

WLC 0.305 0.292 0.262 0.198 HMM

0.304 0.299 0.284 0.219

poor at capturing long-range correlations between the observed variables. Still we believe that the most important aspect to consider in a retrieval system is the quality on the top of the ranking list. Results based on Last.fm tags tend to have lower performances in terms of absolute values. This likely depends on the fact that the semantic descriptions are rather sparse and noisy and that sometimes songs were represented through a uniform distribution. 3.4

Seed Song Retrieval

In this experiment, retrieval is carried out by submitting to the system 50 randomly selected seed songs and considering the sequence of states highlighted by the optimal path as a ranking list of retrieved documents. A ground truth, against which retrieval results are compared, has been created for each query song by selecting the 30 most similar songs according to their human-based annotations. Semantic similarity has been computed using an application of the KL divergence to the set of tags for each pair of songs. We compare different approaches: the HMM-based retrieval, a direct contentbased retrieval where songs have been ranked according to their acoustic similarity with the seed (AB), a semantic similarity measured as KL divergence between the semantic descriptions of the seed song and each document in the collection (TAG), and a linear combination between the two distances (WLC). Additionally we alos include random baseline (Random). As it can be seen from the results reported in Table 2, even in this case the proposed model leads to outperforming results; the same consideration reported in Section 3.3 can be extend to the current evaluation. The only different aspect is that, in this case, the Last.fm tags better quantize the similarity relationships

36

R. Miotto and N. Orio

among songs; thus, the absolute values of the metrics is not very different between the two semantic representations.

4

Conclusions

We introduce a novel methodology that represents a music collection through an hidden Markov model with the purpose to build a music retrieval system that combines content-based acoustic similarity and context-aware semantic descriptions. In the model, each state represents a song, transitions probabilities depend on acoustic similarity and observation probabilities represent semantic descriptions. An application of the Viterbi algorithm allows us to create paths across the model, which provides a ranking list of the songs. This approach represents an application of cross-domain retrieval combining audio content and text for item-based retrieval. It is important to note that the approach can be generalized also to other multimedia tasks where content can be combined with context, such as video or image retrieval. The model can be used as a part of a music digital library to refine the retrieval functions. Some issues are still open and will be addressed in future work. First of all, evaluation tested only the effectiveness of the model; scalability needs to be evaluated with a larger collection, in terms of number of songs and tags. Moreover, future research will be also devoted to the analysis of the effects introduced by different content descriptors and similarity measures. Finally, the extension to other music retrieval tasks, such as music recommendation and playlist generation, will be explored.

References 1. Barrington, L., Oda, R., Lanckriet, G.: Smarter than genius? Human evaluation of music recommender systems. In: Proceedings of the International Conference on Music Information Retrieval, pp. 357–362 (2009) 2. Barrington, L., Lanckriet, G., Turnbull, D., Yazdani, M.: Combining audio content and social context for semantic music discovery. In: Proceedings of ACM SIGIR, pp. 387–394 (2009) 3. McFee, B., Lanckriet, G.: Heterogenous embedding for subjective artist similarity. In: Proceedings of the International Conference on Music Information Retrieval, pp. 513–518 (2009) 4. Slaney, M., Weinberger, K., White, W.: Learning a metric for music similarity. In: Proceedings of the International Conference on Music Information Retrieval, pp. 313–318 (2008) 5. Mandel, M., Ellis, D.P.W.: Song-level features and support vector machines for music classification. In: Proceedings of the International Conference on Music Information Retrieval, pp. 594–599 (2005) 6. Hoffman, M., Blei, D., Cook, P.: Content-based musical similarity computation using the hierarchical dirichlet process. In: Proceedings of the International Conference on Music Information Retrieval, pp. 349–354 (2008)

Accessing Music Digital Libraries by Combining Semantic Tags

37

7. Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Semantic annotation and retrieval of music and sound effects. IEEE Transactions on Audio, Speech, and Language Processing 16, 467–476 (2008) 8. Ness, S.R., Theocharis, A., Tzanetakis, G., Martins, L.G.: Improving automatic music tag annotation using stacked generalization of probabilistic svm outputs. In: Proceedings of ACM MULTIMEDIA, pp. 705–708 (2009) 9. Rabiner, L.: A tutorial on hidden Markov models and selected application. Proc. of the IEEE 77, 257–286 (1989) 10. Shifrin, J., Pardo, B., Meek, C., Birmingham, W.: HMM-based musical query retrieval. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries, pp. 295–300 (2002) 11. Miotto, R., Orio, N.: Automatic identification of music works through audio matching. In: Proceedings of the European Conference on Digital Libraries, pp. 124–135 (2007) 12. Montecchio, N., Orio, N.: A discrete filter bank approach to audio to score matching for polyphonic music. In: Proceedings of the International Conference on Music Information Retrieval, pp. 495–500 (2009) 13. Raphael, C.: Automatic segmentation of acoustic musical signals using hidden markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 360–370 (1999) 14. Khadkevich, M., Omologo, M.: Use of hidden markov models and factored language models for automatic chord recognition. In: Proceedings of the International Conference on Music Information Retrieval, pp. 561–566 (2009) 15. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006) 16. Kullback, S., Leibler, R.: On information and sufficiency. Annals of Mathematical Statistics 12, 79–86 (1951) 17. Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Towards musical query-bysemantic description using the CAL500 data set. In: Proceedings of ACM SIGIR, pp. 439–446 (2007) 18. Manning, C., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press (2008) 19. Turnbull, D., Barrington, L., Lanckriet, G.: Five approaches to collecting tags for music. In: Proceedings of the International Conference on Music Information Retrieval, pp. 225–230 (2008)

Improving User Stereotypes through Machine Learning Techniques Teresa M.A. Basile, Floriana Esposito, and Stefano Ferilli Department of Computer Science, University of Bari “Aldo Moro” Via E. Orabona, 4 – 70125 Bari, Italy {basile,esposito,ferilli}@di.uniba.it

Abstract. Users of Digital libraries require more intelligent interaction functionality to satisfy their needs. In this perspective, the most important features are flexibility and capability of adapting these functionalities to specific users. However, the main problem of current systems is their inability to support different needs of individual users due both to their inability to identify those needs, and, more importantly, to insufficient mapping of those needs to the available resources/services. The approaches considered in this paper to tackle such problems concern the use of Machine Learning techniques to adapt the set of user stereotypes with the aim of modelling user interests and behaviour in order to provide the most suitable service. A purposely designed simulation scenario was exploited to show the applicability of the proposal. Keywords: Digital libraries, Stereotypes, Machine Learning.

1

Introduction

Digital libraries are becoming increasingly important and pervasive information resources in current society. One of the main obstacles in supporting an effective use of digital libraries is their having typically been designed to provide a uniform access style for all users. However, it is known that the variety of user types and contexts affects both their interaction with a system and the effective response from the digital library for any particular user. As a consequence, a personalization task is necessary in order to improve digital library services. To this aim, a preliminary requirement is a better understanding of the user model. Most of the works on this topic face the problem by modelling the user preferences starting from the user feedbacks, that is the user interest, with respect to the results of the performed search. However, often this kind of personalization is not understood by all the users. In fact, usually users are not willing to provide feedback to the system, not even for receiving a better service. Furthermore, users do not necessarily know what their interests are and how they change over time, and hence cannot provide such an information to the system. Finally, even if the user is aware of his interests, the amount of information available in current digital libraries makes it unrealistic for him to specify his preferences completely on every (even if few) query results obtained by the system. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 38–48, 2011. c Springer-Verlag Berlin Heidelberg 2011 

Improving User Stereotypes through Machine Learning Techniques

39

A way to overcome such a limitation could be represented by the exploitation of models built on behaviours instead of explicitly declared user interests. Indeed, more realistic models should take into account information that changes over time, including: cognitive abilities (e.g.: learning styles, perception); individual differences (e.g.: age, gender, education); subject domains (e.g.: arts, health, engineering); work tasks (e.g.: writing an essay, hobby); work environments (e.g.: university, hospital, business office). A challenge, in this respect, is the inability of the systems to meet individual user expectations at run-time. A step in this direction could be done by exploiting machine learning techniques, but this requires approaches that are specific to the task. For example, machine learning could be used to build the initial model by identifying clusters of users, to allow the interface to find out more about the users and to allow reactivity and adaptivity of the system. As to the last step, i.e. the adaptivity and reactivity of the system, a particular issue is that the set of models that are built on user behaviours cannot remain static in the sense that during actual use of the intended interaction strategy more knowledge can be gathered, which should in turn be used to improve the set of models. Moreover, the acquisition of data about the user interest and behaviour can be supported by the user or automatically performed by the system. In the first case the user is asked to execute some action typically providing a feedback on the result. The second method relies on the application of intelligent inference techniques to acquire information about the user by analysing the behaviour. To this regard, in this work we would propose the exploitation of machine learning techniques to improve and adapt the set of user model stereotypes by making use of user log interactions with the system. To do this, a clustering technique is exploited to create a set of user models prototypes; then, an induction module is run on these aggregated classes in order to improve a set of rules aimed at classifying new and unseen users. Furthermore, the approach presented in this paper exploits the knowledge extracted by the analysis of log interaction data without requiring an explicit feedback from the user.

2

Related Work

The exploitation of user models might be very useful to improve the interaction between the user and the system itself, in order for the latter to adapt more easily and straightforwardly the functionalities that it implements to the former. Building user models, however, is a very difficult task, because very often a person’s behaviour and preferences change in time and according to different environments, situations and objectives. For these reasons, automatic learning of user models is a hot research topic and many different approaches and techniques have been proposed in the literature to accomplish this task. In this work, we focus our attention on the strategies that use the concept of stereotype to model and categorize users in order to provide them the most suitable service. Previous works only exploited the stereotypes initially defined by the domain expert to perform a matching with the facts about the user collected during his interaction with the system.

40

T.M.A. Basile, F. Esposito, and S. Ferilli

In the last decade, there was an increasing interest in exploiting machine learning techniques in user stereotype theory in order to make the systems completely autonomous in adapting to different user needs. In [1], a stereotypical user model component is used to store the prediction on the user’s preferences inferred from prior information about user categories. This prediction is then combined with the predictions of other modules, one of which inferred with a probabilistic machine learning approach, in order to estimate the user’s preferences to be employed in the personalization of the services. In [2] a framework for the initialization of student models in Web-based educational applications is presented. The basic idea of the proposal is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbour algorithm. In particular, a student is first assigned to a stereotype category concerning his knowledge level of the domain being taught. Then, the model of a new student is initialized by applying the distance weighted k-nearest neighbour algorithm among the students that belong to the same stereotype category as the new student. In [3] cognitive styles, levels of expertise and gender differences are examined in the stereotype building process and three clustering techniques (k-means, hierarchical clustering and fuzzy clustering) are exploited to understand user behaviour and perception. In [4], machine learning approaches are scheduled to automatically acquire user stereotypes and communities from users’data. Specifically, unsupervised learning techniques on data containing user interests are used to infer user communities. All the works above reported assume that the data useful to build user stereotypes are acquired by means of a feedback provided by the users on the results or a questionnaire answered from the users about their interests. Differently, the approach presented in this paper proposes to exploit the knowledge that is extracted by the analysis of log interaction data, without requiring an explicit feedback from the user, in a cascaded unsupervised and supervised machine learning techniques to improve the set of user stereotypes.

3

The Framework

The general framework we propose, depicted in Figure 1, is made up of a module aimed at creating a set of user classes, followed by a module devoted to generate, on these classes, a set of rules to be used for classifying the behaviour of new and unseen users. Specifically, it consists of an aggregation phase of the users for which an interaction log is provided, an induction phase trained on the general user data eventually obtained during the login phase and aimed at inferring rules to characterize new users, and finally an updating module to modify the feature values and confidences by means of statistical measurements. In detail, the first module concerns the application of clustering techniques to identify similarities among users. Indeed, the aggregation of users that show a similar behaviour could be useful to better understand the conditions under which specific personalized services and interfaces of the system can be proposed to the users. Once clustering has taken place, for each cluster a set of rules

Improving User Stereotypes through Machine Learning Techniques

41

Fig. 1. Schematic representation of the proposed framework

is induced that will be able to identify relations between members of a same stereotypical class. Furthermore, on clustered data, a module based on statistical measurements computation is executed to update the definitions characterizing the stereotypical classes. 3.1

The Stereotypes

The whole process is based on the idea of stereotype [5]. A stereotype is made up of a body (the characterizing component) and a trigger (the prediction component). The body represents the characteristics shared by all the users belonging to the stereotype. The trigger describes the pre-conditions to be satisfied by a user in order to be assigned to a stereotype. Specifically, the trigger will contain the expert-coded rules that are able to assign a user to one or more stereotypes with a probability estimation of the membership, while the body is made up of the features characterizing the stereotype, their values and relative confidence. Formally: • a stereotype is a pair S = (B, T ) where B represents the body and T represents the trigger; • a trigger is a pair T = (P, C) where P is the pre-condition (a proposition or rule) allowing the assignment of the stereotype to a user and C is the confidence with which the membership is assessed; • a body is a set of triples B = {(F, V, R)} where F is the feature (or facet ) describing a property of the stereotype, V is the related value and R is the confidence of having value V for feature F .

42

T.M.A. Basile, F. Esposito, and S. Ferilli

The static profile of a user, i.e. its membership to one or more stereotypes, is obtained by means of a matching process aimed at predicting the user preferences and the way of interaction. The matching is performed between the initial user features (e.g. age, sex, work, education, handicap, etc.) and a set of fixed (and expert-defined) stereotypes describing the main user classes (e,g, gender, education, job, etc.). Specifically, the matching is performed by means of a set of activation rules encoded by an expert (and contained in the trigger component of the stereotype) that will activate the association of a stereotype to one or more users, and of a user to one or more stereotypes. In the latter case, each of the fired stereotypes is to be interpreted as a partial model of the user: multiple stereotypes could be fired and, consequently, combined in order to provide a complete model for the user. Specifically, according to the stereotype combination proposed in [5], the following formulations are used to evaluate the value and confidence of features in the user profile: – if only one stereotype was activated, or the value Vj of a feature Fj is defined in only one of the activated stereotypes: S1=(B1 , T1 ); T1=(P1 , C1 ); B1 = ((F1 , V1 , R1 ), (F2 , V2 , R2 ), . . . , (Fk , Vk , Rk )), then the confidence userRj of having value Vj for feature Fj to be assigned to the user profile is obtained as: userRj = C1 ∗ Rj

(1)

– if n stereotypes were activated, all assigning the same value Vj to feature Fj S1 = (B1 , T1 ); T1 = (P1 , C1 ); B1 = ((F11 , V11 , R11 ), . . . , (Fk1 , Vk1 , Rk1 )) S2 = (B2 , T2 ); T2 = (P2 , C2 ); B2 = ((F12 , V12 , R12 ), . . . , (Fk2 , Vk2 , Rk2 )) ... Sn = (Bn , Tn ); Tn = (Pn , Cn ); Bn = ((F1n , V1n , R1n ), . . . , (Fkn , Vkn , Rkn )), then the confidence userRj of having value Vj for feature Fj for the user profile is obtained as:  userRj = 1 − (1 − Rj ) (2) i=1...n

– if there were n stereotypes activated, assigning different values Vj to feature Fj — say the set of values for feature Fj is {Vji | (Fj , Vji , Rji ) ∈ Si , i = 1, . . . , n} — then for each different value of Vji corresponding to feature Fj the confidence userRji is computed according to equation (2) above reported. Then the value Vj of feature Fj to be assigned to the user profile is chosen as the one to which corresponds the maximum confidence value, i.e. max(userRji ), while the associated confidence in the user profile userRj is userRj =| Ragree − R−agree |

(3)

where Ragree is the confidence of the stereotypes that contain the chosen value Vj for feature Fj and R−agree is the confidence of the stereotypes that contain a different value than Vj for feature Fj .

Improving User Stereotypes through Machine Learning Techniques

3.2

43

Automatic Improvement of the Set of Stereotypes

The problem of the user stereotypes resides in the fact that they are manually built by the personal experience of a domain expert in both the trigger and body components. To overcome such a limitation an automatic procedure, able to generate new and/or modify existing stereotypes exploiting user log interactions with the system, would be desirable. As reported in [6], new stereotypes must be generated by considering long term user features, i.e. features that do not change frequently over time, while short term user features have to be considered in the specialization of the stereotype for a specific user. Furthermore, if the user is not new to the system, a set of user logs, that were collected in the previous user interactions, is available. Thus, the value and confidence of the features in his profile could be automatically modified and, more importantly, new stereotypes can be automatically built. Specifically, according to the above distinction of the set of the features in two distinct types, two steps can be identified in the user model building process: firstly, the user long term features can be used to generate a generic user model stereotype and, subsequently, one can continue by exploiting short term features to improve and personalize it. As a consequence, also the machine learning techniques to be exploited in the user model stereotype construction must be properly selected with respect to the two kinds of available information. Thus, two distinct learning steps have to be planned: in the former, the general data about the users will be used to group and identify stereotypical classes of users not yet considered. Subsequently, the data about the users belonging to each of the groups that were identified in the previous step will be used as training examples to infer rules that will make up the trigger component of the new discovered stereotypes in order to determine how to assign the new users to them. To this aim, hierarchical clustering [7] and decision trees [8] were exploited. Such techniques have the advantage to be robust with respect to the uncertainty in the data and, in some versions, they are able to deal with incremental availability of data, both fundamental characteristics in this domain. Once the new classes of stereotypes are conceptually defined (by means of clustering step) along with the trigger components (by means of rule induction step), the feature values and the associated confidence, that are reported in the user logs, can be used to modify the body component of the new stereotypes, this way allowing to assign such values and confidence to the new users. Specifically, the user logs will be used as background knowledge and a set of statistical measures were identified to update the values along with the confidence of the features. As regards the values of the features, the mean of the values was used. Specifically, let n be the number of users already known to the system that have value Vj for feature Fj , and m be the number of new users (u1 , u2 , . . . , um ) for which the available stereotypes assign respectively values (aj1 , aj2 , . . . , ajm ) to feature Fj . The updated value Vj of feature Fj is as follows:  (n ∗ Vj + i=1,...,n aij )  Vj = (n + m)

44

T.M.A. Basile, F. Esposito, and S. Ferilli

As to the relative confidence Rj for feature Fj , we suppose that it is inversely related to variance: a greater variance corresponds to a lower confidence in that value, and vice versa. Accordingly, the following rule was designed to update the confidence value Rj :   σj σj  Rj = Rj + α ∗ −  Vj Vj where σ and σ  represent the standard deviation of the values of the n known users before the update and of the values of the m new users after the update, respectively. The underlying idea is that a variance on the new users greater than that on the previous ones will contribute to reduce the confidence level, and on the contrary a variance on the new users lower than that on the previous ones will contribute to increase the confidence level. Indeed, σj /Vj and σj /Vj correspond to the percentage of variability with respect to the mean values of the known and new users. α represents a weight of how much the difference between the known and new values has to be considered in confidence updating, by magnifying or softening it. This formulation was improved so that the weight of the two terms is in relation with the number of cases (number of known and new users) that the term represents. The new formulation is as follows:   σj n σj m  Rj = Rj + α ∗ ∗ − ∗ n + m Vj n + m Vj

4

An Exploitation Scenario

Let us now better explain the use of the framework components in a typical scenario. When a user connect to the system, it can occur two cases: he is a new user or not. In our setting, the profile can be built on user generalities, eventually acquired by means of a preliminary registration phase, and on information gathered from an eventually performed psycho-attitudinal test, purposely designed with a domain expert, aimed at characterizing the user profile with specific ability values and confidence. It is worth to note that this information is required once for the user and that the user, however, cannot accept to answer. Suppose to have a new user, then he has three possibility after login: 1) he can register by filling a questionnaire with his generalities (age, sex, work, education, handicap, etc.); 2) he can provide his generalities and perform a specific test defined by the experts in order to improve the personal profile; or 3) he can register by providing only name/surname - that is equal to not provide any information. In the first case, the generalities provided by the user are used as knowledge base to activate one or more stereotypes from the set of available stereotypes (both initially provided by the expert and automatically built) that are successively combined according to the combination rules reported in Section 3.1 to generate the initial user profile. In the second case, the information gathered by the results of the performed test are used to fill in the facets’ value and confidence in the user profile. Finally, in case of absence of any kind of information at the

Improving User Stereotypes through Machine Learning Techniques

45

login phase, a general stereotype (such as a any person stereotype) is assigned to the user and his profile is filled in with default values as setted in the system. Then, the user is let to interact with the system and as soon as a set of log is available, they will be exploited according to the procedure reported in Section 3.2 to modify his profile. On the other hand, in case of a not new user his interaction log are used to improve his profile according to the procedure reported in Section 3.2. In our simulation scenario, each user is represented by two sets of information items: general user data (such as age, sex, job, language, education, job) that are used to provide a value to the trigger component of the stereotypes and to assign a user to one or more stereotypes; and a set of features chosen in such a way that could be used to profile user attitudes in a multimedia environment. In particular, the chosen features are: – textual/audio/video/image cognitive ability: how many texts/audio/videos/ images the user is able to acquire; useful to understand the preference of the user with respect to the textual/audio/video/image information; – symbolic/pointing/spoken expressive ability: agreeableness level of the symbolic/pointing/spoken modality in performing queries; – motor ability: estimation of the motor ability of the user; – multi-modal ability: estimation of the multi-modal ability of the user; – work task: estimation of the goal of the user in performing the task. This feature is useful to better understand the level of detail and the amount of information to provide to the user for the specific query. Figure 2 reports an example of the initial static female stereotype. The facets’ values and relative confidence are filled with default parameters as provided by the domain expert. However, as above reported, they can be computed according to the real user profile if the psycho-attitudinal test results are available. In this way, a specific ability level for that category of users can be suggest. As to the machine learning techniques, we exploited the incremental concept clustering algorithm COBWEB [7] and the decision tree learner C4.5 [9]. COBWEB works by incrementally arranging the observations in a classification hierarchy. Each node represents a concept (a class) and is labelled with a probability value that provides a distribution of the values and associated confidence of the features in the body component for the objects that belong to that node. It exploits the following operators to build the classification hierarchy: Merge of two nodes (two nodes are replaced by a node that has as child a node coming from the union of the two nodes and that has as distribution the distribution of the values of the attributes of the objects belonging to the two nodes); Split of a node (a node is split by replacing it with its children); Insertion of a new node (a new node is added for each new object to be inserted in the hierarchy); Shift of an object in the hierarchy (the sub-hierarchy having the object as root is shifted in the node). C4.5 builds decision trees starting from a set of labelled attribute-value training examples exploiting the concept of entropy. It uses the information gain on each feature to set a shifting point of the set of data in sets of lower cardinality. The algorithm proceeds iteratively on the subset thus obtained.

46

T.M.A. Basile, F. Esposito, and S. Ferilli

Fig. 2. An example of stereotype template

These algorithms were applied to the user general data in order to obtain a classifier that can characterize each new user and assign him to the appropriate stereotypical class. Successively, the features, their value and the associated confidence of the user belonging to a class were used to process the new values and confidences for the discovered stereotypes according to the statistical measures reported in previous section. Specifically, the trigger components of the new stereotype are the result of the learning phase, while the body components are the result of the statistical measurements on the clustered data. To test the proposal, we generated two artificial datasets. The examples were generated by simulating the interaction of the users with the system. Specifically, firstly a specific pattern was established to represent different categories of users and successively, on this pattern, the generation of simulated user interaction started randomly choosing other attribute values. In the first experiment, we generate examples from two user pattern interactions representing generic male and female users. In the second experiment, the intent was to model users that interact with the system for different reasons: work or hobby. In the following the results, i.e. the identified stereotypes along with the trigger and body components, are reported for the two experiments. For the first experiment, on 9980 training data, two clusters (stereotypical classes) were identified along with the trigger and body components. As to the trigger component, the identified stereotypical classes differentiate correctly the users in two groups (men and women) with almost the same ability level for the features provided. Identified Stereotypical Classes - Trigger components: gender = m: cluster2 (4986.0) gender = f: cluster1 (4994.0) Identified Stereotypical Classes - Body components:

Improving User Stereotypes through Machine Learning Techniques

47

Stereotype cluster1 (4994 instances) (textual cognitive ability,0.44,0.57). (audio cognitive ability,0.44,0.56). (video cognitive ability,0.44,0.56). (image cognitive ability,0.43,0.56). (symbolic expressive ability,0.44,0.57). (pointing expressive ability,0.43,0.56). (spoken expressive ability,0.44,0.56). (motor ability,0.45,0.56). (multi-modal ability,0.51,0.5). (work task,0.51,0.5). Stereotype cluster2 (4986 instances) (textual cognitive ability,0.45,0.56). (audio cognitive ability,0.43,0.57). (video cognitive ability,,0.44,0.56). (image cognitive ability,,0.44,0.56). (symbolic expressive ability,0.44,0.56). (pointing expressive ability,0.44,0.56). (spoken expressive ability,,0.44,0.56). (motor ability,0.44,0.56). (multi-modal ability,0.5,0.5). (work task,0.5,0.5).

In the second experiment, on 21358 training data, four clusters (stereotypical classes) were identified. In this case, the trigger component turns out to be more complex compared to the previous experiment. Indeed, it correctly grasps the user profile we represented, i.e. it contains the rule that identifies the kind of task the user performs. However, other specializations for the users that interact with the system for hobby were extracted. For example, one of the trigger rules is: “the user is a man interacting for hobby with the system in italian language”. Identified Stereotypical Classes - Trigger components: job = hobby | language = Italian | | gender = m: cluster4 (2719.0) | | gender = f: cluster3 (2712.0) | language = English: cluster5 (5274.0) job = work: cluster1 (10653.0)

5

Conclusions

In order for a digital library to be defined adaptive, it must adapt the services it provides to the users, i.e. it should provide users with optimized service/access according to particular needs of individual users or groups of users. A key issue to reach this kind of adaptivity is personalization. An approach already exploited in the personalization task is based on the idea of stereotype. Usually stereotypes are built manually by the personal experience of a domain expert and this can represent a limitation. To overcome the problem, in this

48

T.M.A. Basile, F. Esposito, and S. Ferilli

work an automatic procedure able to modify the set of stereotypes based on user interaction with the system is presented. Specifically, new stereotypes can be automatically generated by considering the information collected in the logs of the user interaction with the system. A scenario is reported showing the exploitation of the procedure on two purposely designed datasets.

References 1. Ardissono, L., Gena, C., Torasso, P., Bellifemmine, F., Difino, A., Negro, B.: 1 User Modeling and Recommendation Techniques for Personalized Electronic Program Guides. In: Personalized Digital Television – Targeting Programs to Individual Viewers. Human-Computer Interaction, vol. 6, pp. 3–26. Springer, Heidelberg (2004) 2. Tsiriga, V., Virvou, M.: A framework for the initialization of student models in webbased intelligent tutoring systems. User Modeling and User-Adapted Interaction 14, 289–316 (2004) 3. Frias-Martinez, E., Chen, S.Y., Macredie, R.D., Liu, X.: The role of human factors in stereotyping behavior and perception of digital library users: a robust clustering approach. User Modeling and User-Adapted Interaction 17, 305–337 (2007) 4. Paliouras, G., Papatheodorou, C., Karkaletsis, V., Spyropoulos, C.D.: Discovering user communities on the internet using unsupervised machine learning techniques. Interacting with Computers 14, 761–791 (2002) 5. Rich, E.: User modeling via stereotypes. In: Readings in Intelligent User Interfaces, pp. 329–342. Morgan Kaufmann Publishers Inc., San Francisco (1998) 6. Vasilyeva, E., Puuronen, S., Pechenizkiy, M., Rasanen, P.: Feedback adaptation in web-based learning systems. International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 17, 337–357 (2007) 7. Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 139–172 (1987) 8. Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986) 9. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

Displaying Phonological Diachronic Changes through a Database Application Marta Manfioletti and Mattia Nicchio Department of Linguistics and Performing Arts, University of Padua Via Beato Pellegrino, 1 – 35137 Padua, Italy {marta.manfioletti,mattia.nicchio}@gmail.com

Abstract. This paper presents a project which aims to provide a new digital instrument for linguistic research. This new tool will be able to show the historical evolution of a language into one or more daughter languages, and it will allow users to perform a comparative and typological analysis of diachronic processes. The originality of this project is given by two factors: first, its developers are linguists with notions in computer science, which prevents any communication issue between different teams of experts; second, the data feeding database, though derived from well known corpora, have been processed in a specialist way to display the evolution of words from a mother language to the daughter languages. The instrument will account for all the diachronic phonological rules which occur during the word change.

1

Introduction

Computer science has broadened the horizons of many scientific and humanistic disciplines by providing a huge number of new instruments to the experts of all fields of research. The main issue about the development of those instruments is its interdisciplinary nature. To obtain good results a good level of collaboration and communication is needed between the computer scientists developing the digital instrument and the experts of the discipline the instrument is about. The more the experts of both fields mutually share their specific knowledge, the more accurate and detailed the structure will be. The best solution, however, would be to have a single team with all the necessary competences: this means that the digital instrument would be wholly conceived by the same people, and thus there would be no discrepancies or lack of detail that would require further adjustments or even compromise the functions of the instrument. The division of the work through the areas of expertise can often lead to incomplete or approximate results; the best way to avoid this problem would be for the theoretical structure to be directly conceived according to the structure of the database. This is precisely what is new about this work: the approach to the matter was twofold right from the outset, and allowed the development of a database which performs its tasks while fully accounting for a complex phonological reality. The M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 49–60, 2011. Springer-Verlag Berlin Heidelberg 2011

50

M. Manfioletti and M. Nicchio

effectiveness of this approach becomes even clearer when examining the results: the outcome of the work is not simply the digitalization of a group of corpora, but a completely new research instrument. In this paper we describe the first step of a broader project aiming to create a digital library which gathers diachronic phonological data to potentially account for the historical linguistic changes in every human language. The first phase of the work deals with the design of the database which represents the backbone of the entire digital library. The aims of this first stage will be explained in the next sections, but the most important achievements are listed below: – To automatize the preliminary stages of diachronic phonological analyses and create new knowledge through an original form of data processing. – To allow immediate comparison of data in order to give a broader perspective to comparative and typological research. – To gather information from historical grammars and organize it in a single instrument, thus allowing the comparison of data that have always been isolated to date. – To be able to represent the diachronic phenomena present in every human language. – To account for phonological reality in a new way: for the first time it can be represented by means of the entity-relationship (ER) model. – To explain existing data using new phonological theories [1]. This paper is organized as follows: Section 2 contains the motivations and the purposes of the project; Section 3 introduces some basic linguistic concepts; Section 4 presents the dual nature of the work, explaining the linguistic aspect and accounting for some choices that have been made about the database; Section 5 contains the conclusions and some thoughts about the possible futire developments of this work.

2

Motivations

This section will deal with the preliminary choices that shaped the entire project, and its purpose will be to show how even a simple database structure can give another discipline a new approach to the studied object, thus producing new knowledge. Databases can be extremely useful in a wide range of activities, since they allow users to organize information and to store it so that it will be easy to retrieve and modify later on. However, some distinctions must be made. Consider the case in which a bank wants to organize all the data about every bank account. The ER model will provide the conceptual schema, then a logic schema will be used to design the database structure, which will presumably be quite simple. Eventually, data and metadata will be fed into the database, and the digital tool will be ready to step in and take the place of all the paperwork. This kind of procedure is perfectly ordinary and does not bring any innovation, neither to the banking sector nor to computer science. The only (and yet, not

Displaying Phonological Diachronic Changes through a Database Application

51

minor) improvement as opposed to the previous system is the rationalization of information which was already there at the time of implementing of the database. In the present project, things are different. The contribution given by computer science to linguistics goes much further, since the database will make it possible to create new knowledge. Of course, it is not simply supposed to do such a thing. There are plenty of possible applications of databases to linguistics that do not involve a different perspective on the discipline, but simply allow swifter access to information by digitalizing preexisting corpora. This database, though, aims to give linguists a new way of approaching phonological change and comparing languages and their changing patterns. First of all, the digital tool will be able to store data which has never been gathered in the same place before, and to allow immediate comparison between historical grammars and etymological dictionaries by representing their contents through a common standard. This aspect will not consist of a mere digitalization, since the uniformity of data is yet to be achieved: in fact, human work will be required to extract and elaborate data to make them fit the database structure. There is, however, another main innovation besides the new representation of linguistic data: typological comparison. Once the database is fed with data representing many different languages, it will be possible to compare the evolution of different linguistic varieties in order to make generalizations about the way linguistic changes occur. This approach would not be possible without the digital instrument presented in this paper. Furthermore, the database will help any linguist perform preliminary research before the analysis of a set of phonological changes. Several linguistic databases with different functions already exist or are being created; some of them are listed and described below: – The ASIt project1 [2], which is being carried out by the Department of Information Engineering and the Department of Linguistics and Performing Arts of the University of Padua. The ASIt project consists of a syntactic database where the aim is to gather variants of grammatical structures within a sample of 200 Italian dialects. – The Multimedia Atlas of Venetian Dialects (AMDV2 is being carried out by the Department of Linguistics and Performing Arts of the University of Padua with the collaboration of the ISTC of Rome and the Department of Historical Studies of Ca Foscari University of Venice. The aim of this project is to digitalize the Sprach- und Sachatlas Italiens und der S¨ udschweiz (AIS) by K. Jaberg and J. Jud which represents one of the most important corpora on Italian dialects. The team behind this project has developed a new software (NavigAIS) which enables browsing of a digitalized version of the AIS, thus allowing research based on geographical or lexical criteria. ´ ´ – The Dictionnaire Etymologique Roman (DERom) [3] is a European project that involves several European scholars and especially the Universities of 1 2

http://asit.maldura.unipd.it/ http://www3.pd.istc.cnr.it/navigais

52

M. Manfioletti and M. Nicchio

Nancy and Saarbr¨ ucken. This project goal is to digitalize the Romanisches Etymologisches W¨ orterbuch by W. Meyer-L¨ ubke. – A specific phonological database is the UCLA Phonological Segment Inventory Database (UPSID). This database is a statistical survey of the phoneme inventories in 451 human languages; it was created by Ian Maddieson in 1984 [4].

3

Notes on Phonology

Phonology is the linguistic field that studies the sounds of human languages, and it specifically deals with the sounds which have a distinctive relevance. A particular research field of phonology studies the diachronic processes responsible for phonetic mutation; in fact, languages change continuously and they evolve as time goes by. One of the aims of phonology is to find and motivate the causes that brought a word of a parent language to develop into different shapes in several daughter languages. This particular approach to phonological change is adopted in this work, and it belongs to what is known as diachronic or historical phonology. This work aims to account for two specific aspects of phonology: the chronological order of phonetic and phonological rules and the feature structure of sounds3 . In phonology the ordering of rules represents a main issue, since different rule orders can result in different words. In fact the application of a certain rule can affect the following phonological processes by creating or deleting contexts of application. The rule ordering can be either synchronic or diachronic: since this project deals with historical phonology the speculation involves only the diachronic aspect. The second crucial theoretical aspect that this work aims to underline is the feature structure: sounds are not the minimum unit of analysis, they can be separated into smaller units known as features. Features are the basic phonological units, they are categorized according to the natural class of the segments they describe and they are grouped in force as of their articulatory properties (see Figure 1). The result that is expected to be obtained with this project is a digital library which is able to provide information about the entire process that leads to a word assuming different shapes in different languages from a common origin. The kind of data that will be provided by this application is not available in other databases or books since it represents the result of original linguistic research and processing of data. The innovative aspects of this work are both the design of the database structure and the aim of the instrument. As a matter of fact, the main innovation of this project consists of the new way data are processed, which allows an immediate overview of the diachronic change of specific languages and to account for the rule order and, at the same 3

For Autosegmental Theory see Halle M., Vaux B., Wolfe, A. (2000); for rule ordering see Kenstowicz, 1994 (pp. 89-135) [5] and Odden, 2005 (pp. 225-300) [6].

Displaying Phonological Diachronic Changes through a Database Application

Fig. 1. Feature geometry according to Halle, Vaux and Wolfe (2000)

53

54

M. Manfioletti and M. Nicchio

time, the dual nature of the rules (specific and general representations). It is important to notice that the linguistic data are processed and treated with a specialist approach and for this reason this instrument will be suitable for users who have a specific phonological knowledge. Although this version of the database is filled with data regarding a specific romance reality (in particular this database treats the evolution of words from Latin to Italian and some northeastern Italian dialects), the database structure is designed to be filled with data from every human language.

4 4.1

Approach Linguistic Approach

The aim of the instrument is to account for the phonological changes that occurred during the evolution of a parent language into one or more daughter languages. Theoretically, the structure of the database would allow it to represent phonological diachronic processes taken from any human language (except for tonal processes, which would require a slightly more complex structure), but of course a sample of linguistic varieties was required to test the efficiency of the instrument. The chosen sample consists of standard Italian and dialectal varieties from Veneto and Friuli. Word stems are taken from different corpora and etymologic dictionaries: – Romanisches Etymologisches W¨ orterbuch by Wilhelm Meyer-L¨ ubke; – Sprach- und Sachatlas Italiens und der S¨ udschweiz (AIS) by Karl Jaberg and Jakob Jud; – Atlante Linguistico Italiano by Ugo Pellis; – Lessico Etimologico Italiano (LEI) by Max Pfister; – Dizionario Etimologico Storico Friulano (DESF). The first step in representing diachronic changes is to make it possible to identify the Latin word from which each present word derives. If the processing of the data stopped here, though, we would have nothing more than a digitalized corpus, which would certainly be useful, but not innovative; and that is why this database aims to explain how phonological processes take place, step by step. The theoretical structure of the project is based on two assumptions: every human language is made up of sounds and every language changes over time. Every human language is made up of sounds which are called phones. It is important to clarify that there is a limited set of executable phones, and every language selects its phonetic inventory from that common set (see Figure 2). Furthermore, every language also selects a set of phonemes as a subset of the phone set. Phonemes are distinctive mental units with which the human mind composes words. Phones are the surface realizations of underlying phonemes. If two segments, occurring in the same context, cannot be swapped without changing the meaning of that word, those two segments have a distinctive value, and they are phonemes of that language.

Displaying Phonological Diachronic Changes through a Database Application

55

Fig. 2. Every human executable consonant according to the International Phonetic Association

As we can see in Table 1, [k] and [p] are Italian phonemes since it is impossible to swap the sounds [k] and [p] without changing the word meaning. Table 1. [kane] ∼ [pane]

It is important to underline that a sound can be a phoneme in one language and a simple phone in another, as we can see in Table 2. As a matter of fact [n] represents a phoneme both in Italian and English, whereas [] is an English phoneme but an Italian phone. Table 2. Italian

English

[mano] = mano [ake] = anche

[ n] = thin [ k] = think

Phonological change is determined by rules, in fact words change according to those rules, which are always applied in a given context. When its application context occurs, a rule always applies, and consequently phonological change is regular. The number of possible sounds is limited, and so is the number of natural phonological rules: potentially, all human languages are subject to the same rules.

56

M. Manfioletti and M. Nicchio Table 3. Intervocalic context

Not intervocalic context

AURIC(U)LA > [orek:ja] SPEC(U)LU(M) > [spk:jo] MAC(U)LA(M) > [mak:ja]

CLAVE(M) > [kjave] CLAUSU(M) > [kjuzo] MAC(U)LA(M) > [mak:ja]

To prove that a rule is always applied in a given context we can consider the Italian evolution of the Latin consonant cluster [kl]. As we can see, the Latin consonantal cluster [kl] becomes Italian [k:j] if it occurs in intervocalic context, and [kj] anywhere else (in the beginning of the word or after another consonant). The two different contexts give slightly different outputs of the cluster. It is also essential to consider the order in which phonological phenomena occur, since the application of a rule can create or delete the context for the application of another rule; thus, some processes can only occur after the creation of their optimal context has been performed by a previous process. A practical example is provided by the case of the Latin word ˘ GENUCULUM, which becomes ginocchio in Italian. The actual form of the Italian word is given by the specific order in which phonological rules have occurred over time: without the deletion of the second U, the consonants [k] and [l] would not have formed an intervocalic cluster, and thus would not have formed the Italian sequence [k:j] (see Table 4). Table 4. ˘ GENUCULUM > ginocchio [genukulum] > [dinkkjo] /g/ /e/ /u/ /u/ /kl/ /u/ /m/

[d] [i] [] Ø [k:j] [o] Ø

The structure of the database must also account for a deeper level of analysis. Every phoneme is defined by means of a set of distinctive features. Features are the smallest distinctive phonological unit and they are hierarchically organized following an articulatory criterion, as we have shown in Figure 1. Feature representation of rules provides a wider generalization of the rules themselves: when a rule is expressed using distinctive features it can account for several specific rules (see Table 5). Two word changes are examined in the provided example. In both the input is an unvoiced segment, which eventually becomes voiced. The two inputs /t/ and

Displaying Phonological Diachronic Changes through a Database Application

57

Table 5. STRATA(M) >strada

LACU(M) >lago

/t/

/k/

[d] / V V

[-voiced]



[+voiced] / [-consonantal]

[g] / V V



[-consonantal]

/k/ are different, but they share fundamental articulatory characteristics: they both are stop consonants, and they both are unvoiced. Furthermore, they occur in the same context, i.e. intervocalically. The rule which is expressed through features describes both these cases, and thus it accounts for both of the specific rules. Many specific phenomena can be explained with the same feature representations, which is why the database must provide a correct feature formalization of phonological rules. In conclusion, the linguistic aspects this database must account for are: – the chronological rule order; – feature representation of segments and rules. 4.2

Database Development

The design of the conceptual schema (see Figure 3) required meticulous attention to every phonological aspect and for this reason the first phase of the design consisted of identifying and isolating the main entities, which are: – Root: the Latin word from which the Romance words developed into different shapes; – Word: the outcome that results from the phonological processes; – Rule: the formalization of the phonological change in a linguistic code; it is always composed of a phenomenon which occurs in a context; – Phenomenon: the effective linguistic change; – Context: the linguistic environment in which a phenomenon takes place. Starting from this general schema, a deeper structure was designed considering the two aspects of every phonological change: the concrete and the theoretical. For this reason the conceptual schema shows two separated but specular areas: the first area, shown in the upper part of Fig. 2 (composed of Context, Phenomenon and Rule), aims to represent the concrete phonological reality displaying a specific rule closely related to a particular word; instead the second area (composed of all the entities shown beneath the first area) groups a set of concrete rules into a general and abstract rule. This conceptual bipartition allows two levels of representation which are both essential in the phonological analysis.

58

M. Manfioletti and M. Nicchio




(1,N)

(1,1)

 



(1,N)





(1,N)

 


(1,N)

(1,1)




(1,N)




(1,N)

    
(1,1)










(1,1)

 





(1,1)

(1,N)

(1,N)

(1,1)

(1,1)

  

 








(1,N)

(1,N) (1,N)

(1,1)




  



(1,1)




(1,N)

  
    
(1,1)

(1,1)







(1,N)

(1,N)







(1,N)

(1,N)







(0,N) (0,N)

(1,1)




(1,N)


(1,N)




(0,N)




(0,N)

Fig. 3. Conceptual schema of the database

5

From the Database Application to a Digital Library

This project was conceived as a dissertation for a Master degree, but its development could go well beyond the planned shape it will have once completed. In other words, the completion of this project could be set much further along: although the expected outcome of this work will be a useful instrument for analyzing the language changes that occurred during the transition from Latin to the northeastern Italian dialects and Italian itself, the possible applications of this database are much broader. The main possible implementation consists of a software application whose aim would be to allow any user to use the database with ease. As mentioned before, this instrument is conceived to be used mainly by linguistics experts, since it would be neither useful nor interesting to those who are not performing specialist research in this field. The first task the software will have to accomplish will be to make it easy for linguists to browse the content of the database and to make specific interrogations about the data.

Displaying Phonological Diachronic Changes through a Database Application

59

A further development could involve a set of maps, on which the software should be able to draw isoglosses according to the queries performed by the users. Such an application would allow users to witness the spread of linguistic changes throughout a geographical area. Lastly, a final version of this work should be made available on the web, in order to achieve the most far-reaching aim of the whole project: since the theoretical and practical model of this database is meant to suit all human languages, data insertion should be made possible for every scholar interested in sharing his knowledge. Such a collaboration would grant the digital library a huge amount of detailed information about an extremely wide range of languages from all over the world, thus multiplying and enhancing the opportunities to perform comparative linguistic studies and allowing analysis of the overall distribution of the phonological processes in a new and broader perspective. This objective may seem somewhat ambitious, and it would surely take some time before such a sharing process could begin, but once the guidelines for data insertion were set, the foundation for a common work would immediately be ready. Of course, these predictions need not be taken to their maximum extent: this kind of collaboration can be made on a smaller scale. At any rate, it would still be the starting point of a virtuous circle that would this instrument to reach many universities and, in doing so, help research in this field. In this paper we have shown the first stage of the project, i.e. the database design. The implementations discussed above would enhance the functionality of the digital tool, and would make it an actual digital library with a broad collection of linguistic documents. Such a digital library, as we explained in this work, would not only allow users to easily witness the variety of languages and linguistic changes, but it would also help producing new knowledge by providing linguists new research methods. Acknowledgments. This work is part of our MA dissertation projects which will be defended in the near future at the University of Padua. We would like to thank prof. Maristella Agosti (Department of Information Engineering, University of Padua) and prof. Laura Vanelli (Department of Linguistics and Performing Arts, University of Padua), who are both supervising the projects.

References 1. Halle, M., Vaux, B., Wolfe, A.: On Feature Spreading and the Representation of Place of Articulation. Linguistic Inquiry 31, 387–444 (2000) 2. Agosti, M., Beninc` a, P., Di Nunzio, G.M., Miotto, R., Pescarini, D.: A Digital Library Effort to Support the Building of Grammatical Resources for Italian Dialects. In: Agosti, M., Esposito, F., Thanos, C. (eds.) IRCDL 2010. CCIS, vol. 91, pp. 89–100. Springer, Heidelberg (2010)

60

M. Manfioletti and M. Nicchio

´ ´ 3. Buchi, E., Schweickard, W.: Le Dictionnaire Etymologique Roman (DERom): en Guise de Faire-part de Naissance. Lexicographica. International Annual for Lexicography 24, 351–357 (2008) 4. Maddieson, I.: Patterns of Sounds. Cambridge Studies in Speech Science and Communication. Cambridge University Press, Cambridge (1984) 5. Kenstowicz, M.: Phonology in Generative Grammar. Blackwell, Oxford (1994) 6. Odden, D.: Introducing Phonology. Cambridge University Press, Cambridge (2005)

A Digital Library of Grammatical Resources for European Dialects Maristella Agosti1 , Birgit Alber2 , Giorgio Maria Di Nunzio1 , Marco Dussin1 , Diego Pescarini3, Stefan Rabanus2 , and Alessandra Tomaselli2 1

Department of Information Engineering, University of Padua Via Gradenigo, 6/a – 35131 Padua, Italy {maristella.agosti,giorgiomaria.dinunzio,marco.dussin}@unipd.it 2 Department of Foreign Languages and Literatures, University of Verona Lungadige Porta Vittoria, 31 – 37129 Verona, Italy {birgit.alber,stefan.rabanus,alessandra.tomaselli}@univr.it 3 Department of Linguistics and Performing Arts, University of Padua Via Beato Pellegrino, 1 – 35137 Padua, Italy [email protected]

Abstract. The paper illustrates the methodology at the basis of the design of a digital library system that enables the management of linguistic resources of curated dialect data. Since dialects are rarely recognized as official languages, first of all linguists need a dedicated information management system providing the unambiguous identification of each dialect on the basis of geographical, administrative and geolinguistic parameters. Secondly, the information management system has to be designed to allow users to search the occurrences of a specific grammatical structure (e.g. a relative clause or a particular word order). Thirdly, user-friendly graphical interfaces must give easy access to language resources and make the building of the language resources easier and distributed. This work, which stems from a project named ASIt (Atlante Sintattico d’Italia), is a first step towards the creation of a European digital library for recording and studying linguistic micro-variation.

1

Motivations

In order to make a linguistic resource usable both for machines and humans, a number of issues need to be addressed: crawling, downloading, cleaning, normalizing, and annotating the data are only some of the steps that need to be taken in order to produce valuable content [1]. Data quality has a cost, and human intervention is required to achieve the highest quality possible for a resource of usable scientific data. From a computer science point of view, curated databases [2] are a possible solution for designing, controlling and maintaining collections that are consistent, integral and high quality. In the present contribution we report the ongoing results of a multidisciplinary collaboration which synergistically makes use of the competences of two different teams, one of linguists and one of computer scientists. Some components of the M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 61–74, 2011. c Springer-Verlag Berlin Heidelberg 2011 

62

M. Agosti et al.

teams have previously collaborated in envisioning, designing and developing a Digital Library System (DLS) able to manage a manually curated resource of dialect data in the context of the Atlante Sintattico d’Italia, Syntactic Atlas of Italy (ASIt)1 project, which has collected a considerable amount of syntactic data concerning Italian dialects. This DLS provided linguists with a crucial test bed for formal hypotheses concerning human language. ASIt has demonstrated the need to abstract a specific information space of reference for the management of the linguistic resources. As a result, a new information space implied by a new linguistic project has been framed into an appropriate conceptual model to allow us to develop an enhanced system for the management of the new dialectal resources of interest. The paper reports on this effort giving a short presentation of the European background of interest in Section 2; Section 3 introduces the objectives of the undertaking, Section 4 presents the tagging, and Section 5 introduces the conceptual approach which underlies the digital library system which manages the information space of interest for the effort under development.

2

European Background

The study of dialectal heritage is the goal of many research groups in Europe. The ASIt project is part of the Edisyn network2 which includes similar linguistic research projects developed for Dutch, Portuguese, German and Scandinavian dialects. These projects have been developed according to slightly different goals and methods; today, a full integration is hampered by the different choices made in each project, in particular the tagging system and structure of the respective databases. For instance, the ASIt system has been devised with a tagging system that index-links the whole sentence, in order to deal with phenomena that are not directly associated with a specific morphological element. In contrast, the SAND3 (Netherlands) and Cordial-Syn4 (Portugal) projects are based on a tagging system that isolates and index-links every word. This work has been the first step towards making ASIt compatible with the Edisyn system in order to create a European database for recording and studying linguistic micro-variation. It is also the first time linguistic data from Cimbrian, a language spoken in German language islands of Northern Italy, has been systematically digitalized and integrated into a database.

3

The Linguistic Objectives

The study aims to tag and make available data from two different sources: curated data derived from the ASIt project, which contains syntactic data on 1 2 3 4

http://asit.maldura.unipd.it/ http://www.dialectsyntax.org/ http://www.meertens.knaw.nl/projecten/sand/sandeng.html http://www.clul.ul.pt/english/sectores/variacao/cordialsin/ projecto cordialsin.php

A Digital Library of Grammatical Resources for European Dialects

63

about 200 Italian dialects, and data from a project on the syntax of Cimbrian, a German variety spoken in the language islands of Giazza (Veneto, province of Verona), Lusern (Trentino) and – historically – Asiago/Roana (Veneto, province of Vicenza). Research on the syntax of Italian dialects as well as on the syntax of Cimbrian is of great interest to several important lines of research in linguistics: – it allows comparison between closely related varieties (dialects), hence the formation of hypotheses about the nature of crosslinguistic parametrization; – it allows contact phenomena between Romance and Germanic varieties to be singled out, should they arise; – it allows syntactic phenomena of Romance and Germanic dialects to be found, described and analyzed to a great level of detail. Therefore a project in line with similar projects at the European level was launched to study the creation of a database of syntactic structures which so far have been neglected in traditional dialectological work [3].

4

Overview of the Tagging System

The design of a tagset for corpus annotation is normally carried out in compliance with international standards — e.g. CES (Corpus Encoding Standard)5 — which in turn are based on the specifications of SGML (Standard Generalized Markup Language)6 and international guidelines like EAGLE (Expert Advisory Group on Language Engineering Standard)7 and TEI (Text Encoding Initiative)8 guidelines. According to these standards, each tagset is formed by several sub-systems responsible for the identification and description of different linguistic “units”: text, section, paragraph, clause, and word. Given the objectives of the ASIt and the Cimbrian syntax enterprise, we have focussed on the tagging of sentence-level phenomena as well as tagging at the word level, which according to the EAGLE guidelines should in turn depend on two kinds of annotation: – morphosyntactic annotation: part of speech (POS) tagging; – syntactic annotation: annotation of the structure of sentences by means of a phrase-structure parse or dependency parse. A tagset based on this distinction is normally designed to be used in combination with software applications processing linguistic data on the basis of probabilistic algorithms, which assign every lexical item a POS tag and, subsequently, derive the structure of the clause from the bottom up. The best automatic POS tagger can achieve an accuracy between 95% and 98% which means two to five errors 5 6 7 8

http://www.cs.vassar.edu/CES/ http://www.w3.org/MarkUp/SGML/ http://www.ilc.cnr.it/EAGLES96/home.html http://www.tei-c.org/index.xml

64

M. Agosti et al.

on average every one hundred words. This error is acceptable in case the task is to analyze vast corpora so that practical tasks can be carried out, e.g. roughly translate a collection of texts into different languages or summarize their contents with quantitative analysis (such as frequency or contextual distribution of lexical items). Such an error, however, is not acceptable for some tasks, like the fine-grained tagging of ASIt and Cimbrian data. First of all, it is worth noting that our enterprise has a different objective, being a scientific project aiming to account for minimally different variants of specific syntactic variables within a sample of closely related or geographically adjacent languages. As a consequence, while other tagsets are designed to carry out a gross linguistic analysis of a vast corpus, our tagset aims to capture fine-grained grammatical differences. As a consequence, in order to pin down these subtle asymmetries, the linguistic analysis must be carried out manually. In addition, the corpus of Italian dialects and Cimbrian data would presumably not be big enough to train a probabilistic algorithm. Lastly, the Romance varieties collected in the ASIt project require a different tagset from the tags employed for a German variety such as Cimbrian, since certain morphosyntactic structures are expressed in one language and not in the other. For these reasons a tagset which takes into account the specificity of both Romance and German varieties and which can be assigned manually need to be developed by linguistic experts. 4.1

Tags

The starting point for developing a viable set of tags is the tagset elaborated by the Edisyn project9 , especially for the (Dynamic) Syntactic Atlas of the Dutch dialects (DynaSAND)10 . The ASIt team together with the Cimbrian project team have developed two language-specific sets of tags which are suitable for the Italian dialect data of the ASIt and Cimbrian, respectively, but which at the same time allow our data to be linked to other European databases of dialect syntax. This involves assigning the same denominations to the same parts of speech as in the Edisyn and the ASIt databases, at most adding tags when they are needed for language-specific structures, or leaving out tags which are not relevant for the languages of our project. For instance, the tag “verbal particle” has been added to identify verbal particles which can be found in German dialects (e.g. the verbal particle in the Standard German sentence Ich gehe weg ‘I go away’), but gender values such as ‘masculine’ have been left out for the tag of the past participle, since past participles never inflect for gender in German varieties (compare Standard German sie/er ist gesprungen ‘she/he jumped’ and Standard Italian (lei) `e saltata ‘she jumped’ vs. (lui) `e saltato ‘he jumped’). We can therefore imagine the creation of a language-specific tagset as starting from a universal core shared by all languages, and subsequently developing a language-specific periphery which is compatible with other databases, but which is able to classify language-specific structures. 9 10

http://www.dialectsyntax.org/ http://www.meertens.knaw.nl/sand/

A Digital Library of Grammatical Resources for European Dialects

65

Moreover, the data from ASIt and Cimbrian differ from Edysin and DynaSAND with respect to the internal organization of their tagset, which in the ASIt/Cimbrian projects follows a multidimensional and hierarchical model. First, the ASIt/Cimbrian tagset allows different types of linguistic constituents to be captured, since tags can be assigned to words, phrases, or sentences. This allows the user to retrieve data concerning word, phrase, or sentence level phenomena and formulate complex queries to test the correlation between phenomena at different levels. Second, grammatical features are organized according to a hierarchical structure wherein features are grouped into classes dominated by super-ordinate nodes, e.g. features like ‘masculine’, ‘feminine’, ‘neuter’ are daughters to the same node “gender”. This guarantees an efficient and exhaustive tagging and, above all, it allows the values ‘unassigned’ and ‘unknown’ to be assigned to every node in case that distinction is not relevant in the dialect under observation or a specific value cannot be chosen on the basis of the known data. 4.2

Linguistic Analyses

The tagged corpus of ASIt/Cimbrian data will be available to end users who might be, for example, linguists interested in carrying out syntactic analyses or informants interested in correcting or augmenting the data. Concerning the former, it is important that the data are presented in a way which makes it usable by linguists working in different theoretical frameworks. Although it is inevitable (and, to some extent, also desirable) that the data tagging is influenced by theoretical considerations (in our case, the framework of generative linguistics), it is important that the database be of use to a wider audience than a small group of specialists alone. With respect to the types of structures which can be analyzed in the tagged database, it will be possible to analyze syntactic structures and phenomena in great detail. It should also be possible to deduce morphological paradigms without too much effort, while it still remains a desideratum of further research projects to integrate a component which will make it possible to carry out phonological analyses of the database. Here is an example of what an analysis in these terms of dialect data can look like: pronouns and clitics in Cimbrian. In Cimbrian documents, sentences such as the following can be found ([4], p. 134): miar importar-z-mar nicht zo sterben me matter-it-me not to die ‘I don’t mind dying’ The use of the infinitive particle zo and the expletive pronoun -z- are typical of German varieties, though the postverbal position of the latter partially corresponds to the syntax of northern Italo-Romance varieties, where subject clitic pronouns appear postverbally as well (but only in interrogative and exclamative clauses). Moreover, the doubling of the object pronoun miar, mar could be

66

M. Agosti et al.

evidence of the development of a Romance-like system of object clitics in Cimbrian, unlike Standard German, which does not exhibit pronominal clitics. But notice that the position of the object pronoun mar is not consistent with the position of the corresponding element in Italian dialects, in which object clitics are normally found postverbally only with non-finite verbs.

5

A Conceptual Approach for the Information Space of the Linguistic Project

In this section we report on the work made to define a conceptual approach for the information space entailed by data curated resources of Italian dialects and Cimbrian. To do so, we adopted a two-phase approach: at the beginning the world of interest was studied and represented at a high level of abstraction by means of the analysis of requirements, helped by the use of a website as the point of exchange of information among the people of the two teams; afterwards it was progressively refined to obtain the conceptual representation of its information space, partitioned in five modelling areas, seven main steps of advancements of the project and six actors involved. 5.1

Analysis of Requirements

One of the results of the meetings between the group of computer scientists and the group of linguists has been the definition of a list of common and general requirements for the system which should: – be cross-platform and easily deployable to end users; – be as modular and extensible as possible, to properly describe the behaviour of the service by isolating specific functionalities at the proper layer; – be intuitive and capable of providing support for different tasks and different linguistic objects; – support different types of users who need to have access to different kinds of features and capabilities; – support internationalization and localization allowing the application to adapt to the language of the user and his country or culturally dependent data, such as dates and currencies. On the linguistic side, an accurate work has been made to agree on the tagset presented in Section 4.1 and to arrange it to be usable and automatically interpretable by software tools. Moreover, a number of requirements for the interface were defined together with computer scientists: – the interface should show and preserve the hierarchical structure of the tags during the tagging phase; – the interface should make it possible not only to show the entire hierarchy of the tags but also to navigate the hierarchy level by level by hiding the non-selected nodes and branches;

A Digital Library of Grammatical Resources for European Dialects

67

– it should be possible to manage uncertainty during tagging, for example the system should provide a way to store “unknown” or “not yet assigned” tags; – the interface should alert the user when the tagging of a word is complete, that is all the mandatory features have a value. 5.2

The Conceptual Approach

As a result of the investigation of user requirements and needs, the information space implied by the linguistic project has been framed into a formal model. This model provides a conceptual approach that takes into consideration and describes all the entities involved, and defines “the rules according to which data are structured” [5]. An appropriate conceptual approach is, indeed, the necessary basis for making the produced scientific data an active part of any information enrichment, such as data provenance and citation, management, access, exchange, visualization and interpretation. In this way, data become an integral part of the process of knowledge transfer and sharing towards relevant application communities [6]. The conceptual approach is centred on five main modelling areas of linguistic interest: – linguistic project: This deals with the different aspects of a linguistic research project, such as the collection and organisation of data, the management and subscription of the different types of involved actors, the validation of the work made at the different steps of the project; – dialects: Since dialects are rarely recognized as official languages, linguists need a dedicated information management system providing the unambiguous identification of each dialect on the basis of geographical, administrative and geolinguistic parameters; – documents: This concerns the different documents made available by a project, including questionnaires, interviews, transcriptions of parts of speech, books, and so on. Each document may be translated into different languages or dialects. The same document can be used by different projects and by different editions of the project during the years. Documents are formed by sentences, which are formed by words and eventually grouped into constituents; – tags: Tags are keywords from a controlled vocabulary assigned to sentences, words, and constituents to label, identify, and recognize them; – linguistic analyses: This models the different aspects concerning the linguistic analysis of the experimental results, such as the comparison of results, the statistical analyses, the cartographic representation of selected features, and so forth. Figure 1 represents the different steps of the linguistic enterprise, the actors involved in each step, and the information space entailed. Each project can be viewed as a cycle, starting with a set-up of the project itself and terminating with the presentation of results through search interfaces, maps, raw results and papers, which can be used as starting information for the set-up of a new project. The main steps of the project are:

68

M. Agosti et al. Access to the results of the project Validation of editors’ vork

Tagging of words, constituents and sentences Validation of editors’ vork Segmentation of sentences in words and constituents Retrieval and preparation of written texts, conduction of fieldwork, transcription of audio data, translation of sentences, DB population Setup of a new project

Linguistic Analyses Tags

Documents

Dialects

Visitor

Informant

Editor

Supervisor

System design and model

Administrator

Linguistic Project

Fig. 1. The different steps of the linguistic enterprise, the areas of the information space entailed, and the actors involved in each step

– “Set-up of a new project”: this consists of the creation of the linguistic project itself and on the definition of its users and resources; – “Retrieval and preparation of written texts, conduction of fieldwork, transcription of audio data, translation of sentences, DB population”: in this step the database of documents is populated and enriched with new data from different sources needed to perform next steps; – “Segmentation of sentences into words and constituents”: documents added to the database are, in this phase, split into words and constituents to allow not only the tagging of the entire document or phrase, but a more in depth analysis (Figure 2 shows the interface for editing and splitting sentences into words); – “Validation of editors’ work”: the validation of the definition of words and constituents from sentences, which is the work done in the previous step, is validated and stored in the database; – “Tagging of words, constituents and sentences”: this is the task of assigning tags and labels to the previously created words and constituents (Figure 3 shows the interface for tagging words);

A Digital Library of Grammatical Resources for European Dialects

69

– “Validation of editors’ work on tagging of words and constituents”: with regard to the definition of words and constituents, their tagging also needs to be validated and stored; – “Access to the results of the project”: consultation, browsing and access to all the public information resources produced during the course of the project. The actors involved in the linguistic project, represented by different coloured cubes in Figure 1, will interact with aspects at different levels of the five areas presented above, as summarized by the dark squares on the left side of the figure. The different types of actors modeled and their main tasks are: – the administrator, manages the different aspects of a project such as the setting-up of the project itself, the creation of the users and the administration of the system. Before the start of a project, the administrator is in charge of the design and implementation of the system itself or of its plugins and extensions, and once the project is started, the administrator works in the background to support the work of the other actors; – the supervisor contributes to the creation of the database of sentences by collaborating with the informant on editing interviews, finding books or providing translations, then making the transcription into the database and validating the work made by editors on sentences; – the editor takes part in the project to create words and constituents from given sentences, and to provide the required tags for them. In case of doubts or errors, an editor can communicate with the supervisors to inform them and or to receive help and support, also from the administrator if needed; – the informant is a speaker of a dialect who is asked to produce dialect utterances or to translate one or more sentences into his or her dialect. The informant is usually interviewed and supervised by a linguistic expert; – the visitor needs to consult, browse and access all the public information resources produced during the course of a project in a suitable way. He needs a simple and intuitive interface, and a set of tools to view and compare results, export and print them. Figure 1 also shows that while the early stages are mainly devoted to the setup of the project itself and to the preparation of the data, requiring limited interaction between the different actors; as time passes and the project comes into full swing, there is a progressive increase of interactions and supervising actions made by the actors, represented by the presence of more than one cube representing a type of user. 5.3

The Conceptual Schema

The component of the digital library system that manages and stores the data is based on a relational database. The design and implementation of the curated database of dialectal resources followed a three phase approach:

70

M. Agosti et al.

(a)

(b) Fig. 2. The interface for editing (Figure 2a) and splitting (Figure 2b) sentences

– the world of interest was represented at a high level by means of a conceptual representation based on the analysis of requirements, – afterwards the world of interest was progressively refined to obtain the logical model of the data of interest, and – lastly, the relational database and the interface to access the data were implemented and verified. The core of the schema was developed and presented in [7]. It consists of three broad areas: i) the point of enquiry, which is the location where a given dialect is spoken; ii) the administrative area (namely, region, province), the location belongs to; iii) the linguistic area, i.e. the linguistic group the dialect belongs to. In this work, the information about tags and words has been integrated in the original schema. In particular, the conceptual schema now also models: the the words of a sentence, the hierarchy of the tags, the association between tags and words.

A Digital Library of Grammatical Resources for European Dialects

71

Fig. 3. The interface for tagging the words of a sentence: the words of the sentence are shown on the left; the hierarchy of tags are shown on the main area of the screen

5.4

Pilot Study

An initial run of a study with the purpose of verifying that the functionalities of the system are well-designed has been prepared. The aim is to test the two phases of the information phase presented in Section 5.2 that are currently implemented in the system: the “segmentation of sentences in words”, and the “tagging of words”. A form was prepared to gather non-numeric qualitative data about user opinions, views and list of problems and observations. The form consists of seven questions about: – the evaluation of the functionalities of orthographic correction and segmentation of sentences; – the evaluation of the functionalities of word tagging and the hierarchical organization of tags; – how the interface helps the user to avoid and/or correct mistakes during the two phases. The form was distributed to six linguistic experts which are currently working in entering the data in the system: three professors, one researcher, one PhD student and one master degree student. The results can be summarized as follows: – Positive aspects • the functionality of the orthographic checking of the sentences is valuable and easy to use;

72

M. Agosti et al.

• the functionality of the segmentation of sentences into words is important although it requires to the user some extra effort to clean the text from punctuation marks; • the hierarchical organization of the tags is extremely positive and helps the user during the tagging phase. • the time spent for tagging a sentence is short and it is even shorter as the user learns the position of the tags in the hierarchy; – Negative aspects • the segmentation window can become unmanageable in case of very long sentences; • the system should warn the user during the tagging phase whenever one or more mandatory tags are missing, and/or have a function like ‘jumpto-next-tag’; • the tags saved by the user should be ordered similarly to the hierarchical structure; • there is the need to speed up the phase from one sentence to another. In general, the judgments about the functionalities and the interface are positive. The issue of speeding-up the tagging phase when the user learns the interface has been raised by most of the users. The interface helps the user to correct their mistakes but there is the need to implement the two-level checking, editor and supervisor, for further corrections during the tagging phase (as described in Section 5.2). 5.5

Data

Currently, the linguistic corpus set that has been represented and which is managed by the digital library system is characterized by: – – – – –

468 documents; 48,575 sentences; 530 tags (sentence level and POS level); 16,731 tags for 1,375 sentences for an average of 12.2 tags per sentence; 5,411 tags for 1,501 words for an average of 3.6 tags per word.

Questionnaires, sentences and tagged sentences can be accessed via a Web interface from the ASIt Web site. The team of linguists is currently tagging sentences with a thorough POS tagset by means of a specific interface designed in order to manage both Italian and Cimbrian data11 . When the tagging is completed, the estimated data produced will be hundreds of thousands of tags, i.e. one of the biggest multilingual POS corpus available not only to linguists for linguistic analyses but also machine learning algorithms for training automatic POS taggers. Beside the different data management services and search options, the digital library system also allows the visualization of the geographical distribution 11

http://svrims2.dei.unipd.it:8080/asit/

A Digital Library of Grammatical Resources for European Dialects

73

of grammatical phenomena. This can be done by exploiting the geographical coordinates of each location, which are kept in the data resource. Given these coordinates, the system automatically creates one of the Geotagging formats (GeoRSS12 , KML13 , etc.) and exploits GoogleMaps14 APIs to visualize it. This option is very important because a user can graphically view how the dialects are distributed throughout the country, and perform further analyses rooted on previously presented results [7].

6

Conclusions and Future Work

In this paper we presented the results of an ongoing linguistic project which aims to collect, digitize and tag linguistic data. The project provided the opportunity to merge different fields of research and begin a multidisciplinary collaboration which synergistically makes use of the competences of two different teams, one of linguists and one of computer scientists. Since cross-linguistic comparison will be one of the major interests, the main aim is to design and implement a digital library system that enables the management of linguistic resources of curated dialect data and provides access to grammatical data. For this purpose, a new information space implied by this new linguistic project has been framed into an appropriate conceptual model to allow us to develop an enhanced system for the management of the new dialectal resources of interest: future work will concern the design and development of this DLS for scientific data able to properly support the course of a linguistic project and the cooperation and interaction among researchers, students, industrial partners and practicioners. Once implemented, the usability of the interface will be evaluated in two phases: firstly, by analyzing the activities of the project’s members concerning the supervisioning and the editing of the data; secondly, by studying visitors’ activity by means of log analysis techniques. Acknowledgments. This work has been partially supported by the project “Cimbrian as a test case for synchronic and diachronic language variation proposals for implementing the ASIt (Syntactic Atlas for Italy)” co-financed byo the Fondazione Cariverona, and by the project FIRB “Un’inchiesta grammaticale sui dialetti italiani: ricerca sul campo, gestione dei dati, analisi linguistica” (Bando FIRB Futuro in ricerca 2008, cod. RBFR08KRA 003). The system has been developed by integrating services offered by the IMS Component Integrator (ICI)15 library develped by the Information Management System (IMS) group of the Department of Information Engineering of the University of Padova.

12 13 14 15

http://www.georss.org/ http://www.opengeospatial.org/standards/kml/ http://maps.google.it/ http://ims.dei.unipd.it/software/ici/apidoc/

74

M. Agosti et al.

References 1. Kilgarriff, A.: Googleology is bad science. Computational Linguistics 33, 147–151 (2007) 2. Buneman, P.: Curated Databases. In: Agosti, M., Borbinha, J., Kapidakis, S., Papatheodorou, C., Tsakonas, G. (eds.) ECDL 2009. LNCS, vol. 5714, p. 2. Springer, Heidelberg (2009) 3. Rabanus, S., Alber, B., Tomaselli, A.: Erster Veroneser Workshop Neue Tendenzen in der deutschen Dialektologie: Morphologie und Syntax. Zeitschrift f¨ ur Dialektologie und Linguistik 75, 72–82 (2008) 4. Bidese, E.: Die diachronische Syntax des Zimbrischen. T¨ ubinger Beitr¨ age zur Linguistik (TBL), vol. 510. Gunter Narr Verlag, T¨ ubingen (2008) 5. Tsichritzis, D.C., Lochovsky, F.H.: Data Models. Prentice Hall, Englewood Cliffs (N.J.) (1982) 6. Agosti, M., Di Nunzio, G.M., Ferro, N.: Scientific Data of an Evaluation Campaign: Do We Properly Deal with Them? In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 11–20. Springer, Heidelberg (2007) 7. Agosti, M., Beninc` a, P., Di Nunzio, G.M., Miotto, R., Pescarini, D.: A Digital Library Effort to Support the Building of Grammatical Resources for Italian Dialects. In: Agosti, M., Esposito, F., Thanos, C. (eds.) IRCDL 2010. CCIS, vol. 91, pp. 89–100. Springer, Heidelberg (2010)

Taxonomy Based Notification Service for the ASSETS Digital Library Platform Jitao Yang, Tsuyoshi Sugibuchi, and Nicolas Spyratos Laboratoire de Recherche en Informatique, Universit´e Paris-Sud 11 Rue Noetzlin, 91190 Gif-sur-Yvette, France {jitao.yang,tsuyoshi.sugibuchi,nicolas.spyratos}@lri.fr

Abstract. In this paper, we report our taxonomy-based notification service for the ASSETS digital library platform, which is being developed in an EU co-funded project. Notification is a very fundamental functionality for every living digital library which is continuously updated and dynamically interacts with users. The ASSETS platform provides a common notification service and its extensions based on the publish/subscribe pattern as a message notification infrastructure. Our taxonomy based notification service is one of those extensions that enables users to define subscriptions for receiving notifications by using a hierarchically organized controlled vocabulary, namely a taxonomy. Through this service, users can easily subscribe to messages about specific domain of their interest with a small number of terms in a taxonomy. Then system can efficiently filter a stream of published messages to deliver notifications to proper subscribers by taking account of the taxonomy. This service works as an important piece for enabling various advanced features in the ASSETS platform such as personalized new item lists and a digital preservation service. In this paper, we show an outline of the ASSETS notification architecture, and give a description about a model for the taxonomy-based notification implemented in our service.

1

Introduction

A modern digital library system is formed as a stack of various principals including software components, external systems and human beings as users. Message exchange among those principals is sometimes asynchronous, and/or loosely coupled (associations between senders and receivers might be dynamically changed), and/or multicast (one message might go to more than one destinations). The publish/subscribe pattern is a well-known design for enabling such flexible massage exchange. The publish/subscribe pattern can be modelled as interactions among publishers, subscribers, and a message broker (or a notification service). A subscriber expresses his interest as subscriptions and registers them to a notification service. A publisher creates a new message with its description and submits it to the notification service. The notification service compares a description of each message with each subscription, then notifies a subscriber about new messages matching his interest. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 75–86, 2011. c Springer-Verlag Berlin Heidelberg 2011 

76

J. Yang, T. Sugibuchi, and N. Spyratos

We can find this pattern in many services in digital library systems. For instance: 1. Personalized new item list A user subscribes to messages about new items that match his interest. 2. Digital preservation service An expert subscribes to messages about operations affecting items that he is responsible for their consistency. 3. User generated context service A content creator subscribes to messages about reviewing processes of items he has submitted. However required methods to compare descriptions and subscriptions are slightly different in each scenario. In the scenario 3, the notification service needs to find message descriptions that contain a reviewer’s id, a content creator’s id and an item id designated in a subscription. For this purpose, we need only exact matching over ids to implement this scenario. On the other hand, when a user subscribes to “European paintings” in the scenario 1, the notification service should notify him about not only new “European paintings” but also new “French paintings”, “Italian paintings”, “Spanish paintings”, and so forth. Scenario 1 and 2 require a more sophisticated method which takes account of is-a relations (or subsumption relations) to compare terms in descriptions and subscriptions. In the ASSETS (Advanced Service Search and Enhancing Technological Solutions for the European Digital Library), which is an EU co-funded project aiming to improve the usability of Europeana digital library platform by developing software services and user interfaces focused on search and browsing [1], we are trying to satisfy such heterogeneous requirements for notification by introducing a layered architecture to the design of the notification service. In the digital library platform developed by ASSETS, common functionalities shared by all notification services are implemented in the “common notification service”. On the top of this common service, each technical partner in the ASSETS project can develop his own “extended” service which performs more specific application notification. The taxonomy based notification service we would like to report here is one of those extended notification services in the ASSETS platform. This service enables to compare descriptions and subscriptions by taking account of is-a relationships (or subsumption relations) among terms represented in a taxonomy. In the rest of this paper, we would like to show an outline of the ASSETS notification facility in section 2. Then we would like to give a description about a model for the taxonomy based notification implemented in our notification service in section 3. In section 4, we review some related research works. The development of the project described in this paper is still ongoing. In section 5, we would like to conclude this paper with some remarks about what we are going to achieve in a short term, and what we will extend this work in more future work.

Taxonomy Based Notification Service

2

77

ASSETS Notification Facility

Through discussions with the developers from ASSETS partners, we have analyzed several usage scenarios of notification services including scenarios we explained in the last section. Based on the insight coming from this analysis, we have divided the ASSETS notification facility into a common part and a set of specific application parts. Fig 1 illustrates the architecture of ASSETS notification facility.

Subscribers (human/software) subscription

notification subscription

notification subscription

Digital Preservation Notification Service

ASSETS Notification Services

subscription

notification

Taxonomy based Notification Service

notification subscription

notification

taxonomies taxonomies ASSETS Common Taxonomy Manager

ASSETS Common Notification Service message

message

message

message taxonomies

Publishers (human/software)

Fig. 1. Architecture of the ASSETS notification facility

The base of the notification facility is the ASSETS common notification service. On the top of the common notification service, several extended notification services are implemented for more specific application purposes. The common notification service is the single point to submit all messages created by publishers. On the other hand, every notification service accepts subscription requests from subscribers. The common notification service aggregates all published message and performs a light-weight exact matching to compare message descriptions and subscriptions. Each extended notification service works as a client of the common notification service and it subscribes to specific subsets of all published messages. On the extracted part of message streams, each extended service performs more advanced comparison for more specific application notification. This layered design for implementing the publish/subscribe pattern enables 1) decoupling of publishers and subscribers, 2) single-source and multiple-use of massages, and 3) easy development of extended notification service. The taxonomy based notification service is one of those extended services. This service performs taxonomy based matching on condition that publishers, the notification service and subscribers use the same taxonomy. To share the same taxonomy with all principals in the notification workflow, the ASSETS notification facility has a taxonomy manager service which allows clients to register taxonomies and to retrieve registered taxonomies. By using this service, publishers and subscribers can get appropriate terms from a shared taxonomy for making

78

J. Yang, T. Sugibuchi, and N. Spyratos

their messages or subscriptions. Our notification can also access the same taxonomy for taxonomy based matching through the taxonomy manager service.

3

Model of the Taxonomy Based Notification

The implementation of our taxonomy based notification is based on the approach proposed in [2]. The experimental results for evaluating the cost benefit obtained by this approach can be found in [3]. In this paper, we would like to introduce only the taxonomy based notification model used in this approach without details of algorithm and its cost evaluation. Roughly speaking, the notification in our context is an activity to notify a subscriber about published messages whose descriptions match some subscriptions registered by the subscriber. The taxonomy based notification is a variation of notification that tests whether a message matches a subscription or not by taking account of a taxonomy. We would like to start our explanation from the formal definition of taxonomy in our model. 3.1

Terms, Subsumption Relations, and Taxonomies

Definition 1. Let T be a set of keywords, or terms. A taxonomy T defined over T is a tuple (T, ) where  is a reflective and transitive binary relation over T , called subsumption relation. Given two terms s and t, if s  t then we say that s is subsumed by t, or that t subsumes s. In our work, we assumes that every taxonomy (T, ) is a tree in which the nodes are the terms of T and where there is an arrow s → t iff s subsumes t in . Taxonomy

Paintings

p Paintings by material

Oil paintings Wartercolors

Paintings by style

Frescoes

Gouaches

m1

Italian paintings

Salon paintings

Bolognese school paintings

Venetian school paintings

m3 m2

French paintings

Paintings by location

m5 m4

Messages

Fig. 2. A taxonomy and messages

m6

Taxonomy Based Notification Service

79

Fig 2 shows the example taxonomy Tp we use in this section. In this example, the term Watercolors subsumes the term Gouaches, Italian paintings subsumes Venetian school paintings and Bolognese school paintings. Due to the transitivity of the subsumption relation, the term Paintings subsumes all terms in the tree including itself. 3.2

Descriptions

In order to deliver a message to appropriate subscribers, we need to provide a description of the message together with the identifier of the message. In informal definition, a description in our model is just a set of terms from a taxonomy. If the message is about an oil painting in a Venetian school style, the description of this message over Tp is {Oil paintings, Venetian school paintings}. A description can be redundant if some of the terms it contains are subsumed by other terms. For instance, the description {Watercolors, Gouaches, Italian paintings} is redundant, as Watercolors subsumes Gouaches. Redundant descriptions are undesirable as they can lead to redundant computations during subscription evaluation. We shall limit our attention to non-redundant descriptions that have reduced sets of terms defined as follows: Definition 2. Given taxonomy (T, ), a set of terms D from T is called reduced if for any terms s and t in D, s  t and t  s. Following the above definition, we shall make non-redundant descriptions by removing all but the minimal terms instead of removing all but the maximal terms. Because the former way produces more accurate descriptions than the other. This should be clear from our previous example, where the description {Gouaches, Italian paintings} is more accurate than {Watercolors, Italian paintings}. Finally, we formally define descriptions as follows: Definition 3. Let m be a message. Given taxonomy T , a description describing m over taxonomy T is a tuple D, m where D is a reduced set of terms in T . The dotted lines in Fig 2 indicate what descriptions actually represent. Conceptually, a description D, m can be thought as a set of pairs (t, m) for all t ∈ D. we can represent a set of those pairs as a binary relation R between terms and message identifiers. The relation Rp illustrated in table 1 is a binary relation that actually stores all descriptions for m1 · · · m6 in Fig 2. 3.3

Subscription and Notification

In our model, a query is just a conjunction of terms from a taxonomy. In the following definition the symbol tail(t) stands for the set of all terms in the taxonomy T strictly subsumed by t, that is tail(t) = {f |f  t}, R stands for the binary relation representing descriptions. Queries are defined over T and they are answered based on R.

80

J. Yang, T. Sugibuchi, and N. Spyratos

Table 1. Relation Rp Term t Oil paintings Oil paintings Gouaches Frescoes Venetian school paintings Italian paintings Bolognese school paintings French paintings Salon paintings Salon paintings

Message Identifier m m1 m3 m2 m4 m3 m4 m5 m6 m5 m6

Definition 4. A query q over T is either a single term or a conjunction of terms from T . Its answer, denoted by ans(q), is a set of documents defined as follows: Case 1: q is a single term t from T , i.e., q = t  ans(q) = if tail(t) = ∅ then {d|(t, m) ∈ R} else {ans(s)|s ∈ tail(q)} Case 2: q is a conjunction of terms, i.e., q = t1 ∧ t2 · · · ∧ tn ans(q) = ans(t1 ) ∩ ans(t2 ) · · · ∩ ans(tn ) For instance, let q = {Oil paintings, Italian paintings} be a query over taxonomy Tp in Fig 2, its answer ans(q) based on Rp in table 1 is ans(q) = {m1 , m3 } ∩ {m3 , m4 , m5 } = {m3 }. A subscription in our model is just a query describing (intentionally) the set of messages of interest to a user. In reality, a user can define his subscription by selecting terms from the taxonomy. The conjunction of the selected terms is the user’s subscription. Actually, henceforth, we shall think of a subscription either as a set of terms (e.g., {Oil paintings, Italian paintings}) or as a query (e.g., Oil paintings ∧ Italian paintings). Now we define notification as an activity to inform about existence of answers for registered subscriptions (or queries). Definition 5. Let q be a query registered as a subscription by subscriber s. For a set of published messages represented as binary relation R, ans(q) ⊆ R answered based on R is notified to s. 3.4

Refinement Relations and Subscription Trees

A na¨ıve implementation of the notification is to test whether each incoming message should be notified or not for every subscriptions. However, if the set of subscriptions is large, and/or the rate of events is high, the system might become quickly overwhelmed.

Taxonomy Based Notification Service

81

In our project, we use a more efficient approach based on the observation that this testing is basically a set membership test (i.e. testing whether a message belongs to a given set of messages). The idea is the following: if we have to perform test membership for every set in a collection of sets, we can save computations by starting with maximal sets first (maximality with respect to set inclusion). Indeed, if a message does not belong to a maximal set then we don’t need to test membership for any of its subsets. In order to implement this idea, we need to define first a notion of refinement between subscriptions. In fact, we need a definition that translates the following intuition: if subscription q1 refines subscription q2 then every event that matches q1 also matches q2 . Definition 6. Let q1 and q2 be two subscriptions. We say that q1 is finer than q2 , denoted q1  q2 , iff ∀t2 ∈ q2 , ∃t1 ∈ q1 |t1  t2 . For instance, the subscription {Gouaches, Venetian school paintings, Bolognese school paintings} is finer than {Watercolors, Italian paintings}. A set S of all subscriptions and refinement relation  over S becomes an upper semilattice. For details, see [4].

q1:

q2:

Oil paintings

Oil paintings

Venetian school paintings

q4:

Oil paintings

q3:

Italian paintings

Oil paintings

Venetian school paintings

Italian paintings

Salon paintings

Salon paintings

Fig. 3. A refinement relation

The graph Gs in Fig 3 represents the refinement relation over subscriptions q1 , · · · , q4 . With this graph, we start testing of a message for notification from q1 which is the root of this graph. However, there are two paths from q1 to q4 in Gs . As demonstrated in this example, the graph of a refinement relation can contains multiple paths leading to the same subscription. For a efficient traversal in a refinement relation graph, we need to extract a spanning tree from the graph. We call this tree subscription tree. An optimal subscription tree depends of probability of a message matching each subscription. An optimized algorithm for obtaining and incrementally updating optimal subscription trees is presented in [2].

82

3.5

J. Yang, T. Sugibuchi, and N. Spyratos

Implementation Approach

A problem in implementing the “optimal” algorithm proposed in [2] is that we usually cannot obtain fixed filtering rates. A filtering rate of a subscription is a probability of a message matching the subscription. If we estimate filtering rates from statistics of past messages, filtering rates change every time when a notification system accepts a message. The optimal subscription tree also might change along with changes of filtering rates. Filtering rates and optimal trees dynamically change during a publish/notification process. A na¨ıve approach to keep trees optimal is to construct a subscription tree from scratch when filtering rates are changed, in other words, when a message comes. Of course, it is not an acceptable solution because of its calculation cost. Another problem is the cost to estimate filtering rates. To estimate a filtering rate of a given subscription, we need to test the subscription with past messages to count messages matching the subscription. It means that the notification system needs to perform many matching tests every time when a user registers a new subscription. In this project, we adopt the following compromise approach for these problem: – Subscription trees are not updated along with changes of filtering rates when a new incoming message comes. – Subscription trees are periodically destroyed and re-constructed by using current instance of filtering rates. – To estimate the filtering rate of a subscription, we use the hit count that indicates the number of messages that have matched the subscription during notification processes. Filtering rates are firstly reflected to subscription trees by adding new subscription nodes to the subscription trees by taking account of current filtering rate based on the algorithm proposed in [2]. However, each change of filtering rates caused by a new incoming message is not reflected to the trees one-by-one. Those changes are reflected in bulk by periodically constructing new subscription trees from scratch. To construct a whole subscription tree is an expensive operation. Therefore, this operation is performed as a house-keeping task at a specific time interval. Regarding filtering rates, we count numbers of messages that match each subscription in notification processes. We directly use this hit count to estimate filtering rate. Let S = {s1 , s2 , . . . , sn } be a set of subscriptions, h(s) be the hit count of subscription s. In this approach,  we simply estimate filtering rate σ(si ) of subscription si as σ(si ) = h(si )/ nj=1 h(sj ). Important difference from the na¨ıve approach is that we do not compare a new subscription with any past messages. We test a subscription only with messages that come after the subscription has been registered. Therefore, a newer subscription usually has a less hit count than older one. This approach is based on an assumption that such bias will be flattened for the long-term period.

Taxonomy Based Notification Service

83

In future work, we would like to evaluate the quality of subscription trees constructed based on this compromise approach by comparing with ones constructed by the optimal approach.

4

Related Work

User notification has been studied for a long time and many systems support this function. The different methods of expressing the interest have led to several subscription schemas. Corresponding to different subscription schemas, the user notification systems can be classified as subject-based and content-based. In subject-based user notification systems, each publication belongs to a certain subject. The subscribers specify their interest in the particular subjects and they receive notifications whenever publications dispatched within the particular subjects. Subject-based notification systems limit the selectivity of subscriptions. The content-based system improves on subject-based system by allowing the subscription to contain complex query on publications’ content. As the amount of information of the information system increase very fast, efficient and timely dissemination of messages is a key to distributing information to assist end-users. The matching computing problem has been investigated a lot. For tree-based data, [5–8] give algorithms for filtering XML documents based on XPath. For graph-structured data, [9–12] provide some methods and algorithms for tackling the graph-structured data filtering. We use subject based schema namely taxonomy based data model as presented in [2] in ASSESTS digital library platform for considering its following practical benefits: – Give suggestions for publication description and interest subscription. With the taxonomy data, the publisher can find some suitable professional terms to describe the contents that going to be published; on the other side, the subscribers can get suggestions during the subscription process, such as the user is interested in the architecture of ancient Greece, however, she is not quite familiar with the arts of ancient Greece, and she does not know exactly the name of the ancient architectures that were constructed in that period, then by browsing and clicking on the node named “Architecture of ancient Greece” in the taxonomy tree, the node can pop out sub-nodes named “Temple of Hephaistos”, “Erechtheion” and the other ancient Greece architecture nodes. Therefore, the taxonomy data model can help the user to find the interesting things and the user can subscribe some of them. – Provide more exact notification messages for the subscribers. As described above, both the publication and subscription description use the terms from a same taxonomy, therefore the notification messages sent to the subscribers can exactly match the interests of the subscriber. Since no one likes spam messages, if the notification messages sent to the users are far from their interests, they may block the service. In addition, if we send a lot of notification messages to the users including the publications that have week matching

84

J. Yang, T. Sugibuchi, and N. Spyratos

to the subscription, then the large volume of messages would make the subscribers overwhelmed and the user may neglect the service. – Improve partially the “cold start” problem. “cold start” problem could be briefly described as at the beginning of the service goes live, there are not a lot of users would like to use the service. Taxonomy based data model can partially resolve the problem is because there are quite a part of people that do not know what to subscribe if just let them think about it by mind, therefore they do not use the service, however if we provide a taxonomy tree for them, possible the users just want to have a short time browsing of the taxonomy tree, while during the browsing, some of the terms may attract the interests of the users, then they use the service. – Avoid “zero notification” problem. If we let the users express their interest with any words they would like to use, there is the possibility that no notification messages would be sent to the users, because the words typed by the users are possible have no connections with our platform or the words are miss typed that the system can not retrieve the relevant publications for the subscription. – Simplify the subscription process. As mentioned above, if let the users express their interests just by thinking it in mind and write it down, there are a large part of the users would have no interest to express their interest, they just do not want to think too much. Therefore we provide a knowledge tree for the users, and by simple clicking operations they can find their interests occasionally, and then subscribe them. The taxonomy based data model can provide a simple and convenient service for the users. From the above description, we can find that the taxonomy data is very important for the success of the whole service, therefore a period dynamically maintain and improve of the taxonomy data is necessary. We will improve the taxonomy data based on the following methods: – Synchronize with the content augmentation of the digital library. Assume there will a large volume of “Middle Ages European paintings” be added to the digital library, while there is no such kinds of terms in the taxonomy tree, the taxonomy should be modified by adding a subtree with the terms describing the newly added contents. – Pay attention to the publishers’ feedback. Some of the publishers are very professional, possible they are not very satisfied with some of the terms in the taxonomy or there are no suitable professional terms as they expected. Then we encourage them to send feedback to us, based on the analysis of their feedback, we improve the taxonomy data. – Take into account the subscribers’ feedback. The subscribers’ feedback is also very important for us, such as some of the users would like to book the notification information that does not exist in the taxonomy data tree, we will collect and analyze their requirements and consider if we can add the relevant contents to the digital library and modify the taxonomy. We also have a lot of professional users that their feedback is very important for our improvement.

Taxonomy Based Notification Service

85

– Analyze the search log. The search records usually give feedback about the interests of the users, on the one side we should analyze if the platform can response the contents that meet the users requirement; on the other side, we should check if the taxonomy data cover all or most of the interests of the users.

5

Conclusion

In this paper, we described an outline of our taxonomy based notification service for the ASSETS digital library problem. The major aim of this work is to make our service not only functional, but also reusable for implementing various features required by modern digital libraries. To achieve this goal, we carefully designed our notification model as an application-independent model. The current model is just a message exchange model and it does not include any concepts specific to digital libraries. Developers can implement actual notification applications on the top of (namely, as a client of) our notification service by associating concepts in each application domain with our model. At present, the development task of our taxonomy based notification service is ongoing. As short-term goal we will finalize the implementation of our notification service, then we will develop a “new item list” service as an example application of our service. Through this short term task we would like to proof our concept and do some preliminary evaluations of performance. In future work we expect that some “spin out” technologies being developed in the ASSETS project are adopted as a part of the Europeana digital library portal. If we have a chance to apply our technology to the real Europeana, it is an exciting challenge to deal with real number of users visiting a big portal by using our technologies. To achieve enough performance under such a massive setting, we might need to introduce additional technique for subscription lookup like hashing or indexing. Acknowledgement. This work was partially supported by the European project ASSETS: Advanced Search Services and Enhanced Technological Solutions for the European Digital Library (CIP-ICT PSP-2009-3, Grant Agreement no 250527).

References 1. ASSETS project, http://www.assets4europeana.eu/ 2. Belhaj-Frej, H., Rigaux, P., Spyratos, N.: User notification in taxonomy based digital libraries. In: 24th Annual ACM International Conference on Design of Communication, Myrtle Beach, SC, USA, pp. 180–187 (2006) 3. Belhaj-Frej, H., Rigaux, P., Spyratos, N.: Fast user notification in large-scale digital libraries: experiments and results. In: 11th East European Conference on Advances in Databases and Information Systems, pp. 343–358. Varna, F.J. Curry House of Scientists, Bulgaria (2007)

86

J. Yang, T. Sugibuchi, and N. Spyratos

4. Rigaux, P., Spyratos, N.: Metadata Inference for Document Retrieval in a Distributed Repository. In: Maher, M.J. (ed.) ASIAN 2004. LNCS, vol. 3321, pp. 418–436. Springer, Heidelberg (2004) 5. Diao, Y., Fischer, P., Franklin, M., To, R.: Yfilter: Efficient and scalable filtering of XML documents. In: 18th International Conference on Data Engineering, San Jose, USA, pp. 341–342 (2002) 6. Hou, S., Jacobsen, H.: Predicate-based filtering of xpath expressions. In: 22nd International Conference on Data Engineering, Atlanta, Georgia, USA (2006) 7. Chan, C., Felber, P., Garofalakis, M., Rastogi, R.: Efficient filtering of XML documents with XPath expressions. VLDB Journal 11, 354–379 (2002) 8. Gupta, A., Suciu, D.: Stream processing of xpath queries with predicates. In: ACM SIGMOD International Conference on Management of Data 2003, New York, USA, pp. 419–430 (2003) 9. Petrovic, M., Liu, H., Jacobsen, H.: G-ToPSS: Fast filtering of graph-based metadata. In: 14th International World Wide Web Conference, Chiba, Japan, pp. 539–547 (2005) 10. Petrovic, M., Burcea, I., Jacobsen, H.: S-ToPSS:Semantic Toronto publish/subscribe system. In: 29th VLDB Conference, Berlin, Germany, pp. 1101–1104 (2003) 11. Wang, J., Jin, B., Li, J.: An Ontology-Based Publish/Subscribe System. In: Jacobsen, H.-A. (ed.) Middleware 2004. LNCS, vol. 3231, pp. 232–253. Springer, Heidelberg (2004) 12. Haarslev, V., Moller, R.: Incremental Query Answering for Implementing Document Retrieval Services. In: International Workshop on Description Logics 2003, Rome, Italy, pp. 85–94 (2003)

SIAR: A User-Centric Digital Archive System Maristella Agosti1 , Nicola Ferro1 , Andreina Rigon2 , Gianmaria Silvello1 , Erilde Terenzoni3 , and Cristina Tommasi3 1

3

Department of Information Engineering, University of Padua Via Gradenigo, 6/a, 35131 Padua, Italy {maristella.agosti,nicola.ferro,gianmaria.silvello}@unipd.it 2 Ufficio Archivi, Regione del Veneto Cannaregio, 168, 30121 Venice, Italy [email protected] Soprintendenza Archivistica per il Veneto, Ministero Beni e Attivit` a Culturali Campo Frari, 3002, 30125 Venice, Italy {erilde.terenzoni,cristinaroberta.tommasi}@beniculturali.it

Abstract. This paper presents the SIAR (Sistema Informativo Archivistico Regionale) project supported by the Italian Veneto Region, the aim of which is to design and develop a digital archive system. The main goal of the SIAR project is to develop a system for managing and sharing archive metadata in a distributed environment. In this paper we report the activities that led to the design and development of the SIAR system, underlining the fundamental role played by the user during this process. Indeed, in the SIAR project the archival users provide continuous feedback that allows us to shape the system on a user-needs basis.

1

Introduction

Digital libraries (DL) are in a state of rapid evolution. Although they are still places where information resources can be stored and made available to end users, current design and development efforts are moving in the direction of transforming them into systems able to support the user in different information centric activities. In the context of digital libraries we need to take into account several distributed and heterogeneous information sources with different community background such as libraries, archives and museums and different information objects ranging from full content of digital information objects to the metadata describing them. Digital libraries are heterogeneous systems with peculiarities and functionalities that range from data representation to data exchange while taking in data management along the way. All these aspects need to be taken into account and balanced to support end users with effective and interoperable digital libraries. In this work we restrict the wide spectrum of research aspects studied by the Digital Libraries to focusing on the archives and specifically on the conceptualization, design and development of a Digital Archive System (DAS); in particular, we emphasize the role of the users throughout this process. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 87–99, 2011. c Springer-Verlag Berlin Heidelberg 2011 

88

M. Agosti et al.

SIAR (Sistema Informativo Archivistico Regionale) is a project supported by the Italian Veneto Region the aim of which is to design and develop a DAS. The main goal of the SIAR project is to develop a system for managing and sharing archival metadata in a distributed environment. Archival metadata are geographically distributed across the Veneto Region and they are preserved in several local archives; the SIAR objective is to develop an information system able to create, manage, access, share and provide advanced services on archival metadata [4]. The SIAR project is the result of an effective synergy between computer science and archival competencies that put the users in the center of the process of ideation, design and development of the DAS – i.e. the SIAR system. In each step of the design and development of the SIAR system there was continuous feedback from the users. We have considered the software engineering practice in order to point out six main phases that characterize the development of the SIAR project.




   

 

 

 
  


 

   

Fig. 1. The six main phases carried out within the SIAR project

In Figure 1 we can see the collaboration between computer scientists and archivists in the center of the six main phases that led to the realization of the

SIAR: A User-Centric Digital Archive System

89

SIAR system. At the same time, it also represents the continuous feedback with the users in each one of the six phases: Ideation: in this phase we defined the goals of the project and the direction that it has to follow. This phase also defines the way in which the project is carried out – e.g. how archivists and computer scientists have to work together or who the users of the system are. In this phase the very nature of the archives and archival description was taken into account and we analyzed the state of the art of digital archives. Analysis of Requirements: in this phase we defined the minimum set of requirements that the SIAR system has to fulfill in order to meet archivists and general user needs. Design: in this phase we set the content and functional configuration parameters of the SIAR system by defining the resources that are exploited by the system and specifying the aspects of the system functionality perceived by the end-users. Data Model: in this phase we defined two new data models based on organization of nested sets. We discussed with the archivists the functionalities and the possibilities of using these set data models to define an innovative methodology to model the archives and the archival descriptions. Development: in this phase we instantiated the defined data model by adopting standard Digital Libraries technologies well-suited to meet the archival requirements. Laboratory interaction: in this phase the SIAR system was tested and its functionalities were tried out by the archival users. Their suggestions and criticisms were taken into account to understand which requirements are satisfied by the SIAR system and where it needs to be revised. This paper is organized as follows: Section 2 describes the ideation phase and also highlights the main characteristics of the archives and archival descriptions. Section 3 points out the requirements that the SIAR system has to fulfill. Section 4 introduces the design choices made in the SIAR project. Section 5 introduces the main features of the data model on which the SIAR system relies and section 6 briefly presents the architecture of the SIAR system. Section 7 reports the outcomes of the laboratory interaction phase where the SIAR system was tested by a group pf archival users. In section 8 we make some final remarks.

2

Ideation

The ideation phase took into account the very nature of archives and archival descriptions. The role of archival users is crucial for understanding the characteristics and peculiarities of archives and thus, for addressing the issues that arise when we move from traditional archives to digital ones. One of the most important aspects is that an archive is not simply constituted by a series of objects that have been accumulated and filed with the passing of time. Instead, it represents the trace of the activities of a physical or juridical

90

M. Agosti et al.

person in the course of their business which is preserved because of their continued value. Because the archival documents are strongly connected with the creation and preservation environment the archives have to keep the context in which their resources have been created and the network of relationships between them in order to preserve their informative content and provide understandable and useful information over time. Archives are in fact made up of series which in turn can be organized in sub-series which are formed of archival units. This implies that each of these entities can only be correctly identified and interpreted in relation to the entity they belong to and from which they inherit certain characteristics [17]. The prevailing solution in the development of DAS was to represent these relationships with hierarchical metaphors which collocate each entity in a hierarchical relationship of subordination with the entity it belongs to – i.e. the archive is modeled by means of a tree structure. In this context, archival description is one of the most important tools that we have to consider when we have to deal with archives and it is defined in [12] as “the process analyzing, organizing, and recording details about the formal elements of a record or collection of records, to facilitate the work’s identification, management, and understanding”; archival descriptions have to reflect the peculiarities of the archive. In a digital environment archivists are used to adopt a metadata standard to encode archival descriptions which is called the Encoded Archival Description (EAD). EAD is a standard released by the Library of Congress in partnership with the Society of American Archivists1 . It fully enables the expression of multiple description levels central to most archival descriptions and reflects hierarchy levels present in the resources being described. Furthermore, EAD reflects the archival structure and holds relations between entities in an archive. In addition, EAD encourages archivists to use collective and multilevel description, and because of its flexible structure and broad applicability, it has been embraced by many repositories [11]. Thanks to these features EAD allows the archivists to represent and manage the fundamental characteristics of archives even in the digital environment. On the other hand, EAD allows for several degrees of freedom in tagging practice, which may turn out to be problematic in the automatic processing of EAD files, since it is difficult to know in advance how an institution will use the hierarchical elements. The EAD permissive data model may undermine the very interoperability it is intended to foster. Indeed, it has been underlined that only EAD files meeting stringent best practice guidelines are shareable and searchable [13]. Moreover, there is also a second relevant problem related to the level of material that is being described. Unfortunately, the EAD schema rarely requires a standardized description of the level of the materials being described. Therefore, access to individual items might be difficult without taking into consideration the whole hierarchy. Moreover, sharing and searching archival description might be made difficult by the deep hierarchical structure of EAD files. Indeed, each EAD file is a hierarchical description of a whole collection of items rather than 1

http://www.loc.gov/ead/

SIAR: A User-Centric Digital Archive System

91

the description of an individual item. On the other hand, users are often interested in the information described at the item level, which is typically buried very deeply in the hierarchy and might be difficult to reach. This approach does not allow us to change the metadata format used for describing the archival resources and it binds together the structure of the archive and the archival descriptions. In this way an archive is treated by a DAS as a monolithic unit that does not allow the very flexibility that archival users require [8]. Furthermore, the plain adoption of the EAD approach turns out to be a barrier towards the effective adoption of standard Digital Library technologies such as Open Archives Initiative Protocol for Metadata Harvesting (OAIPMH) [15] [8]. As described in [9,1], both the variable granularity and the crosslanguage access as well as the variable granularity exchange of archival metadata is precluded by this approach. In the SIAR project we chose to take into account both the positive and the negative aspects of archival practice and in particular of the EAD in order to envision an innovative DAS which allows us to represent and manage the fundamental characteristics of archives and at the same time to overcome some well-known issues.

3

Analysis of the Requirements

The SIAR system is a Digital Archival System (DAS) that has to take into account the characteristics of the archives and their resources. The representation and management of an archive and its resources cannot be apart from the retaining of the inner archival hierarchical structure and the relationships between the archival resources defining the context of an archive. The first requirement pointed out by the archivists directly derives from archival practice: the hierarchical structure in which the archival resources are organized has to be preserved as well as the relationships between them in order to be able to reconstruct the context in which they were created and preserved. In particular, in a digital environment an archival system has to manage the descriptions of the documents represented by means on archival metadata. From the interaction with the archival users we understood that archival descriptions should adopt flexible and, if possible, extensible metadata format in order to deal with the heterogeneity of the archival resources. A DAS has to be able to manage different archival metadata formats at the same time. There can be the chance that a document has to be described in multiple ways by means of different metadata formats. The archivists pointed out that an archive is not only composed of documents and archival descriptions, but it also comprises the organizations, institutions, corporate body, families, people and preservation institutes that contribute to creating an archival resource – i.e. producer subjects – and to preserving and making it accessible – i.e. preservation subjects. Each one of these subjects has to be described by means of a proper metadata format. The archivists highlighted the importance of these descriptions and their relationships with the archival

92

M. Agosti et al.

resources that they produce and preserve. We call these descriptions authority files and they can be seen as access points to the archival resources. A DAS has to be able to represent, encode and manage the authority files in whichever metadata format they are encoded and it has to allow the possibility of defining relationships between the various authority files and between these files and the archival descriptions. The description of archival resources and the authority files requires a major human effort. For this reason the possibility to access, update or delete a description or a resource has to be granted only to those who are explicitly authorized. Furthermore, there are relevant privacy issues such that the consultation of an archive has to be controlled and limited if necessary. Access with variable granularity has to be granted to the archival resources. Furthermore, a DAS has to be able to exchange archival descriptions with different degrees of coarseness and belonging to whatever level of the archival hierarchy without having to exchange the whole archive. Furthermore, it is required to have a mechanism available for reconstructing the archival relationships of an exchanged description whenever this is necessary. In the archival context, especially when we consider a multitude of smallor medium-sized organizations that need to describe and manage their own archives, it is very important to envision a DAS which is economically sustainable. The context in which the SIAR will operate is polyhedral and composed of many small- or medium-sized archival entities that consider the economical sustainability of a system as a primary goal.

4

Design

The design phase took into account the requirements that an archival system has to fulfill as well as the state of the art technologies adopted by digital archives. We have analyzed the possibility of adopting well-known and diffused Digital Library technologies such as the OAI-PMH, in order to support the archival necessities. The design phase was crucial for maintaining the system aligned with respect to the information and functional needs of its end-users. The main purpose of the design phase is to set the content and functional configuration parameters of the SIAR system. The former parameters define the resources that are exploited by the system, like repositories of content, ontologies, classification schemas and authority files. The latter parameters specify aspects of the system functionality perceived by the end-users like, for example, the result set format, the query language, the user profile formats and the document model [6]. The work between computer scientists and archivists was fundamental to define a trade-off between the technological possibilities and constraints and the archival necessities. A consistent part of the work focused on the definition of the metadata formats for archival descriptions and for production and preservation subjects – i.e. authority files. Therefore, in the SIAR system together with the archivists we designed an extensible metadata format for the archival description

SIAR: A User-Centric Digital Archive System

93

which relies on the Italian catalog of archival resources [17] developed in the context of the National Archival Portal2. This choice allows us to use different kinds of metadata formats and at the same time to export the SIAR metadata towards the National Archival Portal. In this way we set the ground for the use of a well-defined and widely adopted metadata format that at the same time can encompass most of all the necessities of the archivists. An important task was the selection of the technologies we choose to rely on in the SIAR system; we evaluated the possibility of using the OAI-PMH protocol to grant variable granularity access and exchange of the archival descriptions and to make the SIAR system interoperable. We pursued this choice by defining a data model which is compliant with the protocol inner functionalities and characteristics.

5

The Data Model

In order to define a data model for the archives we have to carry out two main activities: a descriptive activity and a design activity. The former requires documenting the archives and their documents; the latter requires creating data structures to meet the defined set of requirements.

a

G A

b

D

B

d

C

G

E

C

B

A

D

F

F c

e

(a) Tree

f

E

g

(b) Nested Set Model

(c) Inverse Nested Set Model

Fig. 2. (a) A tree. (b) A graphical representation of a NS-M. (c) A graphical representation of a INS-M.

We defined the NEsted SeTs for Object hieRarchies (NESTOR) framework [10,2,5] which is composed of two set data models called Nested Set Model (NSM) and Inverse Nested Set Model (INS-M); these two set data models allow us to model hierarchically structured resources by means of an organization of nested sets that is particularly well-suited to archives. The foundational idea underlying these set data models is that an opportune set organization can maintain all the features of a tree data structure with the addition of some new relevant functionalities. We define these functionalities in terms of the flexibility of the model, rapid selection and isolation of easily specified subsets of data and 2

The National Archival Portal is a project developed by the Directorate General of the Ministry of Cultural Assets and Activities.

94

M. Agosti et al.

extraction of only those data necessary to satisfy specific needs. In Figure 2 we can see a graphical representation of the two set data models composing the NESTOR framework; they are related to the well-known tree data structure. The first model is the NS-M and it is represented by means of a Euler-Venn diagram which helps us to understand their main characteristics. In the NSM every node of a tree is represented by means of a set and the hierarchical relationships between the nodes are retained by the order inclusion between the sets. The elements belonging to a set represents the resources belonging to a specific devision of a hierarchy. The second model – i.e. the INS-M – is based on the same foundational idea of the NS-M but it reverses its logic. We represent the INS-M throughout a graphical aid called the DocBall [16] that allows us to show the relationships between the sets in the inverse model. In INS-M every node of a tree is represented by means of a set – i.e. a circular sector in the DocBall – and the relationships between the sets are retained by an inclusion order. The INS-M defines a set for the root of the tree and every other set created from the nodes of the tree are supersets of it; instead, in the NS-M all the sets created from the nodes of a tree are defined as subsets of the set created from the root. These differences allow us to point out different properties of the models and to choose the most appropriate one on a case-by-case basis. The set data models are independent from the tree but they are strongly related to it. Together with the archivists we discussed these data models, pointing out that if we apply them to the archives we are able to maintain the hierarchical structure and the context as well as we can do with the tree data structure, but at the same time they granted us new possibilities of overcoming some of the issues that were highlighted in the ideation phase. We analyzed with the archivists how a digital archive can be modeled throughout the set data models defined in the NESTOR framework. For instance, if we consider an archive constituted by several divisions each division contains a bunch of records, we pointed out that we can represent the hierarchical relationships between the archival divisions by means of the sets and the records belonging to them by means of elements belonging to the sets. Indeed, by adopting the NESTOR Framework we represent each division as a set maintaining the hierarchical relationships by means of the inclusion order defined between the nested sets. Each record belonging to a division is represented as an element belonging to the set corresponding to this division. In this context we consider each element as a metadata – defined in whatever format – describing an archival resource. The data model does not impose any metadata format for the archival descriptions and it enables a clear distinction between the structural and the content elements of the archives. These features opened up new possibilities to the archivists that can design different metadata schemas well-suited to their necessities. Another key contribution of the NESTOR framework is to allow the separation between the modeling phase and the technological means chosen for its instantiation.

SIAR: A User-Centric Digital Archive System

6

95

Development

The set data models can be instantiated in different ways in order to enhance their use within a specific application or service of the SIAR system. The architecture designed for the SIAR system is divided into three basic layers: the data exchange infrastructure described in [3,7], the metadata management layer described in [9,14] and the user interfaces layer. The SIAR system architecture relies on the instantiation of the NESTOR Framework based on the joint use of some basic features of OAI-PMH. In the SIAR system we exploit the functionality of OAI-PMH called selective harvesting and its internal organization based on OAI-sets that can be used to express a hierarchical structure as an organization of nested sets [9,2]. As pointed out in [9,2,10] there is an actual integration between the NESTOR framework and the Digital Libraries technologies that allows us to represent and manage the hierarchical structure of the archives by means of organizations of nested sets instantiated by exploiting OAI-PMH inner functionalities. In the SIAR system the archive hierarchy is retained by an organization of nested OAI-sets and the archival descriptions are modeled as metadata belonging to the appropriate sets. The system can handle different metadata schemas and currently we have defined and developed a schema for archival descriptions and different schemas for the production and the preservation subjects. The data model provides us the possibility of changing the metadata format without affecting the structure of the archives or the functioning of the system. This feature was already exploited during the design and development of the system; indeed, the metadata schemas were revised during that time accordingly to new archival needs or in accordance with the new national guidelines released in the context of the National Archival Portal. Thus, this possibility eased the interaction between archivists and computer scientists, allowing us to reconsider design choices without changing the developed system in a substantial way. Furthermore, the instantiation based on OAI-PMH allows us to access and share the archival descriptions with a variable granularity and with the possibility of reconstructing the context of an archival description whenever necessary [10]. The SIAR system defines two user roles: archival users and general users. The former can create, modify and delete the metadata, whereas the second one can only consult the metadata in the system. The archival part of the user interface provides users with several forms where they can insert and modify the archival metadata. These forms are shaped on the basis of the metadata schemas that were designed. Together with the archivists we defined some visual aids to help the user in the insertion of the archival descriptions – e.g. instructions about how to compile the fields of the forms, a graphical representation of the inserted archives where archival divisions can be added to the archival hierarchy or descriptions can be added to a specific division. The insertion of new archival descriptions is guided by the system; for instance, if the root of the archive is a “fonds” the children of this node must be “sub-fonds” or a “serie” but it cannot be another “fonds”. We developed several controlled vocabularies to guide users in the description process.

96

M. Agosti et al.

The SIAR system is developed as a Web application that can be accessed by means of a browser and thus it does not require any particular software or hardware infrastructure to be used. The SIAR system is thus freely accessible and usable by the archival users with the only requirement of the access to the Internet; this feature is very important from the economical sustainability point-of-view as it allows even small organizations distributed in the territory to make use of an open system for describing, managing and sharing their archival resources.

7

The Laboratory Interaction

The laboratory interaction was crucial in the development of the SIAR system because it allowed us to verify if the archival requirements have been satisfied. The laboratory was conducted by both the archival and computer science components of the SIAR project; the former presented the choices made in the definition of the metadata formats to be used for the descriptions and the latter explained how the data model and the architecture of the system were designed and developed to meet user requirements. The users that used the SIAR system were asked to insert some archival descriptions about an archive in which they are working and also the metadata regarding the production and preservation subjects. Each step of the laboratory was characterized by a continuous feedback with the users. The archival users were able to insert all their archival descriptions highlighting some relevant aspects related to the description policies that the SIAR project has to provide. The users easily inserted several archival divisions exploiting the graphical aids provided by the user interface. They pointed out that the use of controlled vocabularies to help the insertion of the description is useful but at the same time it can be problematic. Sometimes archival descriptions have to go beyond the standard archival practice in order to describe some aspects of the archival reality that do not fit a standard model. For instance, there could be the necessity to create a sub-fonds as child of a serie and the system should allow this possibility. Another important observation regarded the definition of the authority files and their relationships with the descriptions; the users pointed out that the availability of a manual mechanism for defining the authority lists would be very useful for the archivists. The extension of the spectrum of user privileges in the system is needed to provide a greater differentiation of user roles – e.g. a user authorized to publish the metadata, a user that can revise the inserted metadata, a user that can insert new metadata that will be revised and published later on. A relevant topic of discussion focused on cross-language access to the archival resources; in the context of the SIAR system we have to consider that documents are written not only in Italian but also in Latin or in various forms of dialect. We need to analyze this aspect together with the archivists and verify which multilingual techniques we can adopt in the system.

SIAR: A User-Centric Digital Archive System

97

The discussion with the users highlighted that the some revisions to the user interfaces are necessary; a very important aspect is to define new paths to reach and consult the archival descriptions in order to help the general user to understand and have access to the archival resources. The graphical aids developed for the insertion of the archival description should be adapted and reconsidered from the consultation point-of-view. The system needs to be improved to become a structure not only for the insertion, exchange and consultation of metadata, but also a tool for their logical reorganization. In other words, it needs to allow archivists to also use SIAR as an aid in the phase of recognition, organization and initial description of the archival material and therefore not only as a tool for the final representation of already completed inventories. This implies that the archival structure being inserted needs to be easily adaptable and modifiable “on-the-fly” as the links between the various units and structures of the fonds become clearer in the archivists mind. The flexibility of the data model upon which SIAR is based makes it possible to dynamically reorganize the structure of the archive without modifying the descriptive metadata. The laboratory interaction has been a fundamental step that allowed us to define the order of priorities for the future activities that have to be carried out in the SIAR project.

8

Final Remarks

The SIAR project is a relevant example of collaboration between archivists and computer scientists for the design and development of a Digital Archival System which takes into account both archival and technical needs. The continuous feedback process allowed us to shape and build the system among the users and not only for the users. The outcome of this way of working is optimum efficacy in the design and development phases and a wider adaptability of the system to new user needs. Acknowledgments. The work reported has been envisaged in the context of an agreement between the Italian Veneto Region and the University of Padua that aims at drawing up innovative information management solutions for improving the end-user access to archives. EuropeanaConnect (Contract ECP-2008-DILI52800) and the PROMISE network of excellence (contract n. 258191) project, as part of the 7th Framework Program of the European Commission, partially supported the reported work. We want to thank Silvia Girardello, Silvia Miscellaneo and Luisa Servidei for their useful suggestions during the laboratory interaction phase; furthermore, they continue to use the SIAR system and point out the aspects that need to be revised.

98

M. Agosti et al.

References 1. Agosti, A., Ferro, N., Silvello, G.: Enabling Cross-Language Access to Archival Metadata. In: Cultural Heritage 2009: Empowering Users: An Active Role for User Communities, pp. 179–183 (2010) 2. Agosti, A., Ferro, N., Silvello, G.: The NESTOR Framework: Manage, Access and Exchange Hierarchical data Structures. In: Proceedings of the 18th Italian Symposium on Advanced Database Systems, pp. 242–253. Societ´ a Editrice Esculapio, Bologna (2010) 3. Agosti, M., Ferro, N., Silvello, G.: An Architecture for Sharing Metadata Among Geographically Distributed Archives. In: Thanos, C., Borri, F., Candela, L. (eds.) Digital Libraries: Research and Development. LNCS, vol. 4877, pp. 56–65. Springer, Heidelberg (2007) 4. Agosti, M., Ferro, N., Silvello, G.: Proposta metodologica e architetturale per la gestione distribuita e condivisa di collezioni di documenti digitali. Archivi 2(2), 49–73 (2007) 5. Agosti, M., Ferro, N., Silvello, G.: Access and Exchange of Hierarchically Structured Resources on the Web with the NESTOR Framework. In: IEEE/WIC/ACM International Conference Web Intelligence and Intelligent Agent Technology, pp. 659–662 (2009) 6. Candela, L., Castelli, D., Ferro, N., Koutrika, G., Meghini, C., Pagano, P., Ross, S., Soergel, D., Agosti, M., Dobreva, M., Katifori, V., Schuldt, H.: The DELOS Digital Library Reference Model. Foundations for Digital Libraries. ISTI-CNR at Gruppo ALI, Pisa (November 2007) 7. Ferro, N., Silvello, G.: A Distributed Digital Library System Architecture for Archive Metadata. In: Agosti, M., Esposito, F., Thanos, C. (eds.) Post-Proceedings of the Forth Italian Research Conference on Digital Library Systems (IRCDL 2008), pp. 99–104. ISTI-CNR at Gruppo ALI, Pisa (2008) 8. Ferro, N., Silvello, G.: A Methodology for Sharing Archival Descriptive Metadata in a Distributed Environment. In: Christensen-Dalsgaard, B., Castelli, D., Ammitzbøll Jurik, B., Lippincott, J. (eds.) ECDL 2008. LNCS, vol. 5173, pp. 268–279. Springer, Heidelberg (2008) 9. Ferro, N., Silvello, G.: Design and Development of the Data Model of a Distributed DLS Architecture for Archive Metadata. In: Agosti, M., Esposito, F., Thanos, C. (eds.) 5th IRCDL - Italian Research Conference on Digital Libraries, pp. 12–21. DELOS: an Association for Digital Libraries (2009) 10. Ferro, N., Silvello, G.: The NESTOR Framework: How to Handle Hierarchical Data Structures. In: Agosti, M., Borbinha, J., Kapidakis, S., Papatheodorou, C., Tsakonas, G. (eds.) ECDL 2009. LNCS, vol. 5714, pp. 215–226. Springer, Heidelberg (2009) 11. Kiesling, K.: Metadata, Metadata, Everywhere - But Where Is the Hook? OCLC Systems & Services 17(2), 84–88 (2001) 12. Pearce-Moses, R.: Glossary of Archival And Records Terminology. Society of American Archivists (2005) 13. Prom, C.J., Rishel, C.A., Schwartz, S.W., Fox, K.J.: A Unified Platform for Archival Description and Access. In: Rasmussen, E.M., Larson, R.R., Toms, E., Sugimoto, S. (eds.) Proc. 7th ACM/IEEE Joint Conference on Digital Libraries (JCDL 2007), pp. 157–166. ACM Press, New York (2007) 14. Silvello, G.: Building a Distributed Digital Library System Enhancing the Role of Metadata. In: BCS-IRSG Symposium: Future Directions in Information Access, pp. 46–53. Published as part of the eWiC Series (2008)

SIAR: A User-Centric Digital Archive System

99

15. Van de Sompel, H., Lagoze, C., Nelson, M., Warner, S.: The Open Archives Initiative Protocol for Metadata Harvesting (2nd ed.). Technical report, Open Archive Initiative, p. 24 (2003) 16. Vegas, J., Crestani, F., de la Fuente, P.: Context Representation for Web Search Results. Journal of Information Science 33(1), 77–94 (2007) 17. Vitali, S.: Archival Information Systems in Italy and the National Archival Portal. In: Agosti, M., Esposito, F., Thanos, C. (eds.) IRCDL 2010. CCIS, vol. 91, pp. 5–11. Springer, Heidelberg (2010)

ASIt: A Grammatical Survey of Italian Dialects and Cimbrian: Fieldwork, Data Management, and Linguistic Analysis Maristella Agosti1 , Birgit Alber2 , Paola Beninc` a3 , 1 1 1 Giorgio Maria Di Nunzio , Marco Dussin , Riccardo Miotto , Diego Pescarini3 , Stefan Rabanus2 , and Alessandra Tomaselli2 1

2

Department of Information Engineering, University of Padua {maristella.agosti,giorgiomaria.dinunzio,marco.dussin, riccardo.miotto}@unipd.it Department of Foreign Languages and Literatures, University of Verona {birgit.alber,stefan.rabanus,alessandra.tomaselli}@univr.it 3 Department of Linguistics and Performing Arts, University of Padua {paola.beninca,diego.pescarini}@unipd.it

Abstract. ASIt aims to observe, collect and analyse the linguistic variation displayed by the dialects of a language. The main theoretical hypothesis is that linguistic variation is not due to chance, but depends on the combination of a finite number of parameters. It is a first step towards the creation of a European digital library for recording and studying linguistic micro-variation.

1

Introduction and Motivation

In order to make a linguistic resource usable both for machines and humans, a number of issues need to be addressed: crawling, downloading, cleaning, normalizing, and annotating the data are only some of the steps that need to be taken in order to produce valuable content. Data quality has a cost, and human intervention is required to achieve the highest quality possible for a resource of usable scientific data. From a computer science point of view, curated databases are a possible solution for designing, controlling and maintaining collections that are consistent, integral and high quality. The ASIt project aims to observe, collect and analyse the linguistic variation displayed by the dialects of a language [1,2,3]. The main theoretical hypothesis is that linguistic variation is not due to chance, but depends on the combination of a finite number of parameters. The study of genetically related dialects constitutes a primary field of research in order to isolate some of these parameters and reach a better understanding of the architecture of the language faculty. This project represents a significant contribution both to the field of Italo-Romance linguistics and, more widely, to formal linguistics by adopting an interdisciplinary approach that interfaces traditional dialectological study with recent developments in morphological and syntactic theory. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 100–103, 2011. c Springer-Verlag Berlin Heidelberg 2011 

ASIt: A Grammatical Survey of Italian Dialects and Cimbrian

2

101

Scientific Challenges

The study of dialectal heritage is the goal of many research groups in Europe; however, a full integration of all the studies carried out by each research team is hampered by the different choices made in each project, in particular the tagging system and structure of the respective databases. Some projects devised a tagging system that index-links the whole sentence; some others are based on a tagging system that isolates and index-links every word [1,3]. One of the challenges of the ASIt project is to create a database for recording and studying linguistic micro-variation, in particular to design a database and a tagging system compatible to the Edisyn network1 which includes linguistic research projects developed for Dutch, Portuguese, German and Scandinavian dialects. Another important objective is the inclusion of linguistic data from Cimbrian, a language spoken in German language islands of Northern Italy [2]. These two main objectives require the design and development of a “curated database” of dialects and languages and an accurate definition of the tagging system. The tags have to focus on both the sentence-level phenomena as well as he word level phenomena, which according to the EAGLE (Expert Advisory Group on Language Engineering Standard)2 guidelines should in turn depend on two kinds of annotation: morphosyntactic annotation, part of speech (POS) tagging; syntactic annotation, annotation of the structure of sentences by means of a phrase-structure parse or dependency parse. The choice of an automatic POS tagger to tag the dialectal sentences is not appropriate for the ASIt project because this project aims to account for minimally different variants of specific syntactic variables within a sample of closely related or geographically adjacent languages. As a consequence, even the best POS tagger with an accuracy of 98% wouldn’t be sufficiently good for fine grained tagging; therefore, in order to pin down these subtle asymmetries, the linguistic analysis must be carried out manually [3].

3

Key Technologies

The ASIt project will promote the following technologies: – comparison between closely related varieties (dialects), hence the formation of hypotheses about the nature of crosslinguistic parametrization; – single out contact phenomena between Romance and Germanic varieties; – find, describe and analyze syntactic phenomena of Romance and Germanic dialects to be found. The tagged corpus of ASIt/Cimbrian data will be available to end users who might be, for example, linguists interested in carrying out syntactic analyses or informants interested in correcting or augmenting the data. Moreover, it is important that the database be of use to a wider audience than a small group of specialists alone; for this reason, the ASIt project will also: 1 2

http://www.dialectsyntax.org/ http://www.ilc.cnr.it/EAGLES96/home.html

102

M. Agosti et al.

– be cross-platform and easily deployable to end users; – be as modular and extensible as possible, to properly describe the behaviour of the service by isolating specific functionalities at the proper layer; – be intuitive and capable of providing support for different tasks and different linguistic objects; – support different types of users who need to have access to different kinds of features and capabilities; – support internationalization and localization allowing the application to adapt to the language of the user and his country or culturally dependent data, such as dates and currencies. The project will also promote the exchange of information between the academic staff, the scientific collaborator and the administrative staff of the project, and also the public dissemination of works and relevant results. On the basis of their grammatical features, the sentences will be indexed according to a tag set and will be stored into a relational database that can be searched through a dedicated system of information retrieval that finds the relevant examples on the basis of the requested tags. The database will finally form an organized set of data that can be easily retrieved and compared in order to allow the extraction of sets of data and generalizations for articles and presentations. The ASIt project will also explore new visualization tools for the analysis of the geographical distribution of grammatical phenomena. This can be done by exploiting the geographical coordinates of each location, which are stored in the database. Given these coordinates, the system automatically can create one of the Geotagging formats (GeoRSS3 , KML4 , etc.) and exploit GoogleMaps5 APIs to visualize it. This option is very important because a user can graphically view how the dialect data are distributed through the country, and perform further analysis based on these visualizations.

4

Contribution by Italian Research Community

The ASIt project is the results of a multidisciplinary collaboration which synergistically makes use of the competences of different linguistic and computer science research teams. Some components of the teams have previously collaborated in envisioning, designing and developing a Digital Library System (DLS) able to manage a manually curated resource of dialect data in the context of the ASIt6 project, which has collected a considerable amount of syntactic data concerning Italian dialects. This DLS provided linguists with a crucial test bed for formal hypotheses concerning human language. ASIt has demonstrated the need to abstract a specific information space of reference for the management of the linguistic resources. As a result, a new information space implied by a 3 4 5 6

http://www.georss.org/ http://www.opengeospatial.org/standards/kml/ http://maps.google.it/ http://asit.maldura.unipd.it/

ASIt: A Grammatical Survey of Italian Dialects and Cimbrian

103

new linguistic project has been framed into an appropriate conceptual model to allow us to develop an enhanced system for the management of the new dialectal resources of interest. One of the main goals of the project is the preparation of a co-ordinated collection of Italian dialects; this co-ordinated collection can be conceived only because the present research team is building on previous and long-lasting research that has produced intermediate and basic results [2,3]. This means that the data the ASIt project has produced is based on long-standing experience of data collection, documentation, and preservation. Another important contribution to the ASIt project is given by the research teams of the German variety of Cimbrian7 . Cimbrian, spoken in the language island of Giazza (Veneto, province of Verona), Lusern (Trentino) and – historically – Asiago/Roana (Veneto, province of Vicenza), is of great interest to different important lines of research in linguistics - a fact which is witnessed by many studies on Cimbrian throughout the last decade. Acknowledgements. Project FIRB “Un’inchiesta grammaticale sui dialetti italiani: ricerca sul campo, gestione dei dati, analisi linguistica” (Bando FIRB Futuro in ricerca 2008, cod. RBFR08KRA 003). Project “Cimbrian as a test case for synchronic and diachronic language variation proposals for implementing the ASIt (Syntactic Atlas for Italy)” co-financed by the Fondazione Cariverona.

References 1. Agosti, M., Beninc` a, P., Di Nunzio, G.M., Miotto, R., Pescarini, D.: A Digital Library Effort to Support the Building of Grammatical Resources for Italian Dialects. In: Agosti, M., Esposito, F., Thanos, C. (eds.) IRCDL 2010. CCIS, vol. 91, pp. 89–100. Springer, Heidelberg (2010) 2. Agosti, M., Alber, B., Di Nunzio, G., Dussin, M., Rabanus, S., Tomaselli, A.: Cimbrian as a test case for synchronic and diachronic language variation: a conceptual approach for the information space. In: Congresso Nazionale AICA 2010. L’Aquila citt` a storica, citt` a digitale, citt` a futura. La ricostruzione dell’Aquila come laboratorio sperimentale per la comunit` a scientifica ed industriale nazionale ICT (2010) 3. Agosti, M., Alber, B., Di Nunzio, G.M., Dussin, M., Pescarini, D., Rabanus, S., Tomaselli, A.: A Digital Library of Grammatical Resources for European Dialects. In: Agosti, M., et al. (eds.) IRCDL 2011. CCIS, vol. 249, pp. 61–74. Springer, Heidelberg (2011)

7

http://ims.dei.unipd.it/websites/cimbrian/

ASSETS: Advanced Service Search and Enhancing Technological Solutions for the European Digital Library Nicola Aloia, Cesare Concordia, and Carlo Meghini Institute for Information Science and Technologies of the Italian National Research Council (ISTI-CNR), Pisa {nicola.aloia,cesare.concordia,carlo.meghini}@isti.cnr.it

Abstract. ASSETS is a 2 year project co-funded by the CIP Policy Support Programme. The main goal of the project is to improve the usability of Europeana (the European Digital Library) by developing, implementing and deploying largescale services focusing on search, browsing and user interfaces. ASSETS strives also to make more digital items available on Europeana by involving content providers across different cultural environments.

1

Introduction and Motivation

Heritage, history and cultural diversity represent all together an enormous yet dispersed strength which unanimously contribute to create the Europe uniqueness. Europes libraries, museums, archives and audiovisual archives possess huge and rich collections, which, once made accessible by the Internet, allow people to consult and reuse them for several different purposes, like work,leisure and study, and for a wide range of information product and services (e.g. tourism, education). European Commission has been acknowledging the importance to preserve, maintain, sustain and make European cultural heritage accessible for some years. In particular the Digital Libraries initiative, which is flagship project of the EU Commission i2010 strategy for the Information Society, is strongly supporting the setting up and the development of Europeanathe European Digital Library, which aims at making accessible to a wide public books, films, newspapers, photographs, drawings and archival documents from European cultural institutions, through a unique web portal. The ASSETS project aims at developing digital library services focusing on search, browsing and interfaces in order to improve the usability of Europeana. ASSETS will implement and deploy largescale services for: – – – – –

Searching multimedia objects based on metadata and on content similarity; Browsing multimedia objects for rapid navigation through semantic crosslinks; Building Interfaces designed for interacting with multimedia objects; Planning Longterm Access to digital information; Ingestion of metadata requiring normalization, cleaning, knowledge extraction and mapping to common structure.

M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 104–106, 2011. c Springer-Verlag Berlin Heidelberg 2011 

ASSETS: Advanced Service Search and Enhancing Technological Solutions

105

The main expected outcomes from ASSETS project can be summarized as follows: – a set of valueadded services to directly enhance the content accessibility and improve the usability of Europeana – a set of operational services adding new multimedia digital content to Europeana, – a set of recommendations, guidelines and best practices concerning the technology adaptation, and service rolling up, to contribute to the promotion of the standards and specifications addressed in Europeana, and pave the way for an increased interoperability.

2

Scientific Challenges

From a scientific point of view, participating to ASSETS gives to researchers the opportunity to have access to very high quality data, such as those made available to the project by the involved stakeholders; and to have direct contact with the users and the stakeholders, thus gaining requirements for new challenges.

3

Key Technologies

Considering the “three-tier architecture” paradigm typically adopted to describe information management systems, the ASSETS system can be described as follows: Presentation tier: The ASSETS system will provide RESTful APIs enabling external applications to use its functionalities, a set of GUIs for managing specific functionalities (e.g. data ingestion and access) and a web GUI (ASSETS portal) for browsing services and functionalities. Business logic: A set of autonomous software components implementing the functionalities described in the previous paragraph interacting each others using an ad hoc integration component. Data tier: An heterogeneous set of repository managers to manage different kind of information objects: multimedia objects, structured data, etc. The ASSETS system is a loosely coupled system: each software component implementing the business logic has no (or little) knowledge of other components and also is not supposed to know which tools are actually used to store or manage data. The ASSETS system rely on the top of the technology outcomes of the PHAROS, MESH, BRICKS, CASPAR, PLANETS and also other European related projects such as eConnect.

106

4

N. Aloia, C. Concordia, and C. Meghini

Contribution by Italian Research Community

CNR-ISTI has a large participation in the ASSETS project. The ISTI team is responsible of several fundamental activities and is involved in all R&D activities. The main ISTI activities are: – Ingestion of metadata and machine learning: The ISTI team will deliver a suite of services implementing sophisticated functionalities for metadata cleaning, knowledge extraction and data classification. The goal of these services is to improve the quality of the content stored in Europeana. – Indexing and retrieval of data: A new search service for Europeana will be provided. The service will use advanced indexing and query processing techniques in order to return the most significant result set and will also provide a similarity-search functionality for multimedia objects. – User Generated Content and personalization: The ISTI team, in cooperation with the University of Paris Sud, will deliver a service managing the ingestion of user generated information objects in Europeana. This service will enable users to create and manage their own objects also in a cooperative way (notifying actions to other users etc) and to submit the object to Europeana, if accepted those objects will be part of the Europeana information space. Another important activity of ISTI is the collaboration to the integration of ASSETS development platforms with the Europeana Labs.

Computational Models Enhancing Semantic Access to Digital Repositories Floriana Esposito, Nicola Di Mauro, Claudio Taranto, and Stefano Ferilli Department of Computer Science, University of Bari “Aldo Moro” {esposito,ndm,claudio.taranto,ferilli}@di.uniba.it

Abstract. The growing amount of heterogeneous digital repositories has created a demand for effective and flexible techniques for automatic multimedia data retrieval. While the primary type of information available in documents is usually text, other type of information such as images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating the visual content from the underlying meaning is wide. The main goal of the project concerns the investigation of machine learning approaches to improve the semantic access to multimedia repositories by combining information gathered from the textual content with the one coming from pictorial representation. Furthermore, they have to be scalable, efficient and robust with respect to the inborn highdimensionality and noise in the data collection.

1

Introduction and Motivation

The rapid expansion of digital data repositories raised problems that go beyond simple acquisition issues, and cause the need to organize and classify the contents in order to improve the effectiveness and efficiency of the retrieval procedure. During the past years a considerable effort was spent in the definition of automatic tools for features extraction from row and unstructured data, such as images, video, audio and text, resulting in the development of systems for content-based retrieval based on indexing and querying engines. However, they lack in dealing with one of the main characteristics of multimedia repositories represented by the existence of relations among the objects contained in the collection. Furthermore, as the volume and the typology of the data increases, memory and processing requirements need to correspondingly increase at the same rapid pace, and this is often prohibitively expensive. Indeed, multimedia digital collections on this scale make performing even the most common and simple indexing/retrieval task non trivial. The National Projects “Progetto di Ateneo 2008 - Modelli computazionali con caratteristiche intelligenti per l’accesso semantico a documenti digitali” and “Progetto di Ateneo 2010 - Metodi e modelli per l’interpretazione semantica di immagini digitali” intended to investigate the applicability of relational models on complex data, such as multimedia digital collections, by proposing efficient M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 107–110, 2011. c Springer-Verlag Berlin Heidelberg 2011 

108

F. Esposito et al.

and robust methods to solve inference and learning tasks on noisy and highdimensional data with the aim of providing meaningful annotations for making more effective the indexing and retrieval procedure.

2

Scientific Challenges

Information retrieval in large digital repositories is at the same time a hard and crucial task. This task is made more difficult by the presence of different object contents (image/video/audio/text). One of the challenges of this research area is represented by the object description. Usually objects are described by low level features automatically extracted from raw data. The problem with them is that they seldom represent the semantic content of the object, which is commonly the focus of a user query. This phenomenon is known as the semantic gap between the object descriptor and the user search criteria. Thus the need of deriving high level semantic features to uniformly describe the objects in the collection. As a consequence, even the relationships among objects in the collection have to be taken into account and properly described and handled for completely outlining the repository. On the other hand, the availability of massive, and consequently, noisy data have to be reshaped the design of indexing and querying approaches. To this aim, dimensionality reduction and statistical theory play pivotal roles.

3

Key Technologies

The development of an efficient and effective framework able to deal with multimedia collections must necessary take into account theoretical models able to take advantage of peculiarities and cope with problems specific of the domain. As to concern the first aspect, i.e. the peculiarities of the domain, it is unquestionable that a lot of relationships among data involved in a multimedia repository exist and that neglecting them can be dangerous for an effective management of the data. Furthermore, the problem of noise and high dimensionality of such kind of collections is a key issue in developing efficient indexing and querying engines. In our perspective, both the presented questions can be successful handled by exploiting relational learning paradigms. Classical relational learning approaches fail in dealing with noisy data and mostly with high-dimensional ones. Hence, the need of investigating the applicability of probabilistic/statistic relational learning techniques to complex data. The representation and use of probability theory makes Statistical Relational Learning (SRL) techniques suitable for combining domain knowledge and data, expressing relationships, avoiding overfitting a model to training data, and learning from incomplete datasets. As for classical probabilistic graphical models, such as Bayesian networks and Markov networks, statistical relational languages exploit structure underlying many distributions one wants to encode. The same structure often allows the distribution to be used effectively for inference, answering queries using the distribution as a model of the world. Finally, this

Computational Models Enhancing Semantic Access to Digital Repositories

109

framework facilitates the effective construction of the models by learning from data a model that provides a good approximation to a past experience thus enhancing the retrieval step in noisy data collections.

4

Contribution by the Research Group

The contribution of the research unit focuses on the issues concerning the task of modelling and reasoning on relationships between images, or between objects within an image, by proposing efficient and robust to noise approaches. In [1] the problem of complexity reduction for image indexing and retrieval was faced. Specifically, the aim of the work was twofold. It firstly investigated the possibility to efficiently extract an approximate distribution of the image features with a consequent indexing error reduction. Successively, the influence of such a resulting approximate distribution on the retrieval step performance based on similarity ranking was analysed. In particular, the image indexing process was improved by using a sampling method to approximate the distribution of correlograms, adopting a Monte Carlo approach to compute the distribution on a subset of pixels uniformly sampled from the original image. A further investigated variant was to sample the neighborhood of each pixel too. Correlograms can be also used in a profitable way as features representing the images in a complex network of relations with the aim of improving image classification tasks. Specifically, in [2] a method to improve the classification accuracy adopting a Statistical Relational Learning approach is explored. The main idea is to assume that the images in a domain are not mutually independent and to try to elicit the hidden information representing the probabilistic connections between two images taking into account the possible relationships. To reach this goal, images are represented by means of a complex probabilistic network, where each image corresponds to a node and the connection degree between images is represented by a probabilistic edge. The relationship degree between images may be computed adopting a similarity measure based on their feature based representation. The ultimately goal was to verify whether modelling the problem of image classification using a SRL language can improve the accuracy of a classical K-Nearest Neighbour (KNN) approach. In particular, we adopted the probabilistic logic ProbLlog as SRL model to describe the structure of the probabilistic network arising from the abstraction process we adopted to represent an image collection. In [3] it was proposed a method for Object Recognition that tries to understand an image by looking for known shapes in it, and relies on a combination of existing and novel image processing techniques, as a preliminary step to describe images using higher-level, human-understandable concepts and relationships among them. In particular, the approach focuses on the identification of potential objects in the image to be exploited in the indexing phase, on their representation and storage in suitable data structures and, lastly, on the definition of a retrieval algorithm that allows to detect known objects in new images.

110

F. Esposito et al.

Acknowledgements. The work was partially supported by the National Projects “Progetto di Ateneo 2008 - Modelli computazionali con caratteristiche intelligenti per l’accesso semantico a documenti digitali” and “Progetto di Ateneo 2010 - Metodi e modelli per l’interpretazione semantica di immagini digitali”.

References 1. Taranto, C., Di Mauro, N., Ferilli, S., Esposito, F.: Approximate image color correlograms. In: Bimbo, A.D., Chang, S.-F., Smeulders, A.W.M. (eds.) Proceedings of the 18th International Conference on Multimedia, pp. 1127–1130. ACM (2010) 2. Taranto, C., Di Mauro, N., Esposito, F.: Probabilistic Inference over Image Networks. In: Agosti, M., et al. (eds.) IRCDL 2011. CCIS, vol. 249, pp. 1–13. Springer, Heidelberg (2011) 3. Ferilli, S., Basile, T.M.A., Esposito, F., Biba, M.: A contour-based progressive technique for shape recognition. In: Proceedings of the Eleventh International Conference on Document Analysis and Recognition (ICDAR), pp. 723–727. IEEE Computer Society, Washington, DC (2011)

The CULTURA Project: CULTivating Understanding and Research through Adaptivity Maristella Agosti and Nicola Orio Department of Information Engineering, University of Padua {maristella.agosti,nicola.orio}@unipd.it

Abstract. CULTURA aims at personalisation and community-aware adaptivity for Digital Humanities through the implementation of innovative adaptive services in an interactive environment. The intention is to offer genuine user empowerment and different levels of engagement with digital cultural heritage collections and communities.

1

Introduction and Motivation

The interdisciplinary field of Digital Humanities is concerned with the intersection of information communication technology, like digital libraries systems, and a wide range of humanities disciplines, including history, and art history. Despite fresh impetus experienced in the field in recent years, current research practices in the humanities still tend to be very labour intensive, solitary and characterised by research material which is often disconnected and non-digitised. This has presented a particular obstacle to novice researchers and the general public. Widespread digitisation represents an important step forward, but the requirement remains for specialist environments which offer a rich, personalised and stimulating engagement with this digitised material. The objective of the CULTURA project is to pioneer the development of personalised information retrieval and presentation, contextual adaptivity and social analysis in a digital humanities context. This is motivated by the desire to provide a fundamental change in the way digital cultural heritage is experienced, analysed and contributed to by communities of interested individuals. These communities typically comprise a diverse mixture of professional researchers, apprentice researchers (e.g. students of history and art history), informed users (e.g. users belonging to relevant societies or interest groups, cultural or authorities) and interested members of the general public.

2

Scientific Challenges

A key challenge facing curators and providers of digital cultural heritage worldwide is to instigate, increase and enhance engagement with digital libraries that M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 111–114, 2011. c Springer-Verlag Berlin Heidelberg 2011 

112

M. Agosti and N. Orio

manage cultural collections. To achieve this, a fundamental change in the way cultural collections are experienced and contributed to by communities is required. Personalisation and adaptive contextualisation technologies such as adaptive hypermedia, adaptive web, intelligent systems, personalised information retrieval systems and recommendation systems have been successful in many application areas such as education, tourism, and general information sites. These technologies reconcile each user’s interests, prior experience or location to provide personalised navigations of relevant digital resources (adaptive personalisation) or to suggest personalised recommendations based on similar users’ behaviour and feedback (social recommendation). However, current adaptive personalisation technologies have two key weaknesses: – They fail to take into account any broader community of which the user is a member, thus neglecting a valuable source of insight into user intention. – They are unaware of the structure and internal dynamics of the material to which they offer access. Such “domain awareness” is an important input to the selection and sequencing of material presented by an adaptive system to the user. It is necessary to be aware of the activity of the entire community of interest which forms around digital humanities libraries. This involves tracking the trends and the points of interest in the community. This information can then be used to identify, retrieve and present material which is likely to be of interest to the individual user. The tracking of the trends will be implemented also through a categorisation of users; users will be categorised in terms of their experience levels and their demonstrated interests, so that the choices of “similar” users can be exploited as an input to the personalised information retrieval process. The resulting system will be one of the next generation adaptive systems that can make digital humanities resources more appealing to the broader public, as well as supporting the activities of professional researchers. This will lead to larger and more active communities of interest focused on the collections managed by the digital resources management system. Such communities are key to not only sustaining interest in our heritage but in promoting deeper understanding of, and contribution to, digital humanities collections.

3

Key Technologies

CULTURA is promoting and integrating the following key technologies: – Cutting edge natural language processing, to normalise ambiguities in noisy historical texts; – Entity and relationship extraction, to identify the key individuals, events, dates and other entities and relationships within unstructured text; – Social network analysis of the entities and relationships within cultural content, and also of the individuals and broader communities of users engaging with the content;

The CULTURA Project: CULTivating Understanding and Research

113

– Multi-model adaptivity to support dynamic reconciliation of multiple dimensions of personalization. Through the environment offered by CULTURA each researcher will not only be able to explore a body of noisy digital cultural data in a personalised and adaptive manner, but also make their own contributions to the body of knowledge around the collection. The environment will significantly advance the current research practices in humanities and offer unprecedented opportunities to empower a broader community of researchers to access and work with cultural artefacts. CULTURA will provide rigorous evaluation and validation of its adaptive services using high impact, contrasting, multicultural digital cultural heritage collections and diverse user communities and individuals. The 1641 Depositions1 and the Imaginum Patavinae Scientiae Archivum (IPSA)2 are the collections and communities to be used. The use cases, defined in collaboration with real users, will clearly illustrate how the adaptive environment will offer genuine user empowerment and unprecedented levels of engagement with these collections and communities.

4

Contribution by Italian Research Community

The prototype of IPSA has been used as a research tool by scholars in the history of art. Efforts have been made to present it to other researchers in Italy and Europe. Building on the experience in using the actual version of the prototype, the next step in our ongoing project is to study how to extend its functions to develop it as an education and dissemination digital library. At the same time, we will elaborate on actual functions to address a number of research questions, which can be addressed by automatic tasks to help scholars to discover new knowledge. In this study process, IPSA can be considered as a case study to learn new ways of using and extracting information of interest from new categories of users. A further step will be to generalize the findings of this case study to similar digital cultural heritage collections and applications. Using IPSA as a new starting point to develop tools for researchers in illuminated manuscripts, we began a new analysis of requirements on the research questions that should be addressed by a digital library of this illuminated manuscripts. The analysis was carried out on a focused group of scholars and professional users, including professors in the history of illumination, in the history of medieval art, and experts in digitised manuscripts [1]. The initial results of this ongoing study highlighted some priorities. The research questions confirmed the results of our initial analysis of requirements, introducing additional concepts to refine the existing tools. The user group underlined that images are the main subject of scientific research on illuminated manuscripts. Although text surrounding the image is important as well, it has to be noted that in many cases the author of a manuscript 1 2

URL: http://1641.tcd.ie/about.php URL: http://www.ipsa-project.org/index.php

114

M. Agosti and N. Orio

copied the text from pre-existing manuscripts, while the illustrator added original drawings. These drawings can be copied, with some modifications, from previous images, or just be inspired by them. A second group of research questions regarded the relations between the content of the digital library and external collections. Illustrators could be inspired by manuscripts that are part of other collections, but also by other art forms of the same historical period. The main research question related to this point can be expressed in two main forms, regarding either the possibility of finding relations with other digital libraries or the possibility of querying the digital library using external information. The analysis of requirements highlighted that automatic tools, which mine the content of online collections, can be a valuable tool for researchers [2]. At the same time, the scientific research on illuminated manuscripts can take advantage of any kind of documentation that can be related to the content of the manuscripts. The possibility of having this information available when studying an image is considered of great importance. Models of the digital humanities content artefact, identifying the items which are most popular, most important and which attract the most annotation are going to be defined in the context of the CULTURA project. The actual version of IPSA already supports an annotation mechanism that is very simple and not comprehensive of the necessary primitives that are going to be identified and managed in the prosecution of the project. Acknowledgements. CULTURA project3 is funded within the Seventh Framework Programme of the European Commission, Area “Digital Libraries and Digital Preservation” (ICT-2009.4.1), grant agreement no. 269973.

References 1. Agosti, M., Mariani Canova, G., Orio, N., Ponchia, C.: A case study for the development of methods that improve the user engagement with digital cultural heritage collections (poster). In: International Workshop on Multimedia for Cultural Heritage (MM4CH 2011). University of Modena, Italy (2011) 2. Agosti, M., Mariani Canova, G., Orio, N., Ponchia, C.: Methods of personalizing a collection of images using linking annotations. In: Agosti, M., De Luca, E.W., Lawless, S., Leveling, J. (eds.) Proceedings of the First Workshop on Personalised Multilingual Hypertext Retrieval (PMHR 2011), In Conjunction with the ACM Hypertext 2011 Conference, pp. 10–17 (2011)

3

CULTURA Project Website, URL: http://www.cultura-strep.eu/

Project D.A.M.A.: Document Acquisition, Management and Archiving Michelangelo Ceci, Corrado Loglisci, Stefano Ferilli, and Donato Malerba Department of Computer Science, University of Bari “Aldo Moro” {ceci,loglisci,ferilli,malerba}@di.uniba.it

Abstract. A paper document processing system is an information system component which transforms information on printed or handwritten documents into a computer-revisable form. In intelligent systems for paper document processing this information capture process is based on knowledge of the specific layout and logical structures of the documents. In this project we design a framework which combines technologies for the acquisition and storage of printed documents with knowledge-based techniques to represent and understand the information they contain. The innovative aspects of this work strengthen its applicability to tools that have been developed for building digital libraries.

1

Introduction and Motivation

The large and increasing amount of paper documents to be processed daily demands new document management systems that are able to catalogue and organize them automatically based on the semantics of their contents. Personal document processing systems that can provide functional capabilities of classifying, storing, retrieving, and reproducing documents, as well as extracting, browsing and synthesizing information from a variety of documents are in evergrowing demand. However, they operate on electronic documents and not on paper ones. This is the focus of the Document Image Analysis (DIA) area, which investigates the theory and practice of recovering the symbol structure of digital images scanned from paper or produced by computer, and hence results in the conversion of document images to symbolic form for modification, storage, retrieval, reuse and transmission. This conversion is a complex process articulated into automatic and semi-automatic stages which we have explored in the project “D.A.M.A. Document Acquisition, Management and Archiving”, funded by the Data Service S.p.A. company (Mantova, Italy).

2

Scientific Challenges

Document Image Analysis deals with the recognition of logically and semantically relevant components in the layout extracted from a document image. This opens several challenges. The representation of recognized and extracted information into some common data format is a key issue. A solution to this problem M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 115–118, 2011. c Springer-Verlag Berlin Heidelberg 2011 

116

M. Ceci et al.

can come from the XML technology. XML has been proposed as a data representation format in general, but it was originally developed to represent (semi-) structured documents, therefore it is a natural choice for the representation of the output of DIA systems. XML is also an Internet language, a feature that can be profitably exploited to make information drawn from paper documents more quickly web-accessible and retrievable than distributing the bitmaps of document images on a Web server. Moreover, it is possible to define hypertext structures which improve document reading. Finally, in the XML document, additional information on the semantics of the text can be stored in order to improve the effectiveness of the retrieval. This is a way to reduce the so-called semantic gap in document retrieval, which corresponds to the mismatch between a user’s request and the way automated search engines try to satisfy it. The extraction of semantics from the document image requires knowledgebased technologies, which offer various solutions to the knowledge representation problem and automated reasoning, as well as to the knowledge acquisition problem by means of machine learning techniques. The importance of knowledge technologies has led to the proliferation of machine learning and data mining methods which, especially with classification approaches, provide suitable tools for the recognition of components and understanding of the content. The representation formalism used in these classification approaches is another issue constantly discussed. The spatial dimension of page layout makes formalisms used in inductive logic programming and multi-relational data mining the most suitable candidate for modeling documents in DIA.

3

Contribution by the Research Group

In the project, we have designed a framework of tools to integrate information based on the understanding of the document content. This framework offers functionalities to digitize paper documents, acquire them as document images and interpret them with sophisticated and intelligent techniques. Tools for understanding the content of documents allow to integrate structured, semi-structured and unstructured information stored in different repositories. Among the acquisition functionalities, the possibility to extract atomic pieces of information and the mechanisms of contextualization permit to simplify the data entry operation and to reduce human intervention. Here we describe some of the functionalities of the framework which are available in the prototype system IDIS [1,3]. Document image analysis is performed through a process composed by the preprocessing of the raster image of a scanned paper document, the segmentation of the preprocessed raster image into basic layout components, the classification of basic layout components according to the type of content (e.g., text, graphics), the identification of a more abstract representation of the document layout (layout analysis), the classification of the document on the ground of its layout and content, the identification of semantically relevant layout components, the application of OCR only to textual components and the storing in XML format providing additional information on the semantics of the text (Figure 1).

Project D.A.M.A.: Document Acquisition, Management and Archiving

117

In particular, initial processing steps include binarization, skew detection and noise filtering. Before the actual interpretation of text data takes place, graphic data that are present in the digitized document must be separated from the text so that subsequent processing stages may operate exclusively on textual information. The separation of text from graphics is performed in two steps: image segmentation and block classification. The former is the identification of rectangular blocks enclosing content portions while the latter aims at discriminating blocks enclosing text from blocks enclosing graphics (pictures, drawings, . . . ). In order to facilitate subsequent document processing steps, it is important to classify these blocks according to the type of content: text block, horizontal line, vertical line, picture (i.e., halftone images) and graphics (e.g., line drawings). The classification of blocks is performed by means of a decision tree automatically built from a set of training examples (blocks) of the pre-defined classes. The result of the segmentation process is a list of classified blocks, corresponding to printed areas in the page image. These blocks are processed in order to detect structures among them by means of layout analysis techniques. In IDIS, we integrate an hybrid approach composed by a global analysis technique, which determines possible areas containing paragraphs, sections, columns, figures and tables, and a local analysis technique, which groups together blocks that possibly fall within the same area, called frames. The result is a hierarchy of abstract representations of the document image, the geometric (or layout) structure. The leaves of the layout tree (lowest level of the abstraction hierarchy) are the blocks, while the root represents the whole document. After having detected the layout structure, the logical components of the document, such as title, authors, sections of a paper, can be identified. The logical components can be arranged in another hierarchical structure, which is called logical structure. The logical structure is the result of repeatedly dividing the content of a document into increasingly smaller parts, on the basis of the human-perceptible meaning of the content. The leaves of the logical structure are the basic logical components, such as authors and title. The heading of an article encompasses the title and the author and is therefore an example of composite logical component. Composite logical components are internal nodes of the logical structure. The root of the logical structure is the document class (e.g. ‘scientific paper’, ‘letter’ or ‘censorship card’). The problem of finding the logical structure of a document can be cast as the problem of associating some layout components with a corresponding logical component [2]. In IDIS, this mapping is limited to the association of a page with a document class (document classification) and the association of frames with basic logical components (document understanding). The first stage is that to label the pages with a document model (class). This operation is performed by exploiting an inductive logic programming approach of supervised classification which permits to represent documents and models: the class of a document is identified through a positive matching test between logic formulas. A second stage of classification allows to recognize logical components. This step is generally performed by encoding a knowledge base that defines the mapping from

118

M. Ceci et al.

the layout to the logical structure. In IDIS, we integrate a machine learning approach which creates a knowledge base by means of an inductive process that learns rules in first-order logic from layout information of manually classified documents. The matching between these rules and the description of the document layout determines the recognition of logical components. Another stage of classification is done to understand document images by recognizing semantically layout components (e.g., ‘title’, ‘authors’ in a scientific paper). In this case we consider also textual features besides of layout information as done in the previous stage and as classifier we integrate the well established Support Vector Machine. The result of document processing is stored in XML format so to include semantic information extracted in the document analysis and understanding processes and make it accessible via web technologies [2].

Fig. 1. Intelligent Document Interpretation Framework

References 1. Ceci, M., Berardi, M., Malerba, D.: Relational data mining and ILP for document image understanding. Applied Artificial Intelligence 21(4&5), 317–342 (2007) 2. Esposito, F., Malerba, D., Semeraro, G., Ferilli, S., Altamura, O., Basile, T.M.A., Berardi, M., Ceci, M., Di Mauro, N.: Machine learning methods for automatically processing historical documents: From paper acquisition to XML transformation. In: DIAL, pp. 328–335. IEEE Computer Society (2004) 3. Malerba, D., Ceci, M., Berardi, M.: Machine learning for reading order detection in document image understanding. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. SCI, vol. 90, pp. 45–69. Springer, Heidelberg (2008)

DDTA - Digitalisation of Districts in the Textile and Clothing Sector Floriana Esposito1 , Stefano Ferilli1 , Nicola Di Mauro1 , Teresa M.A. Basile1 , and Marenglen Biba2 1

Department of Computer Science, University of Bari “Aldo Moro” {esposito,ferilli,ndm,basile}@di.uniba.it 2 Computer Science Department, University of New York Tirana [email protected]

Abstract. The main goal of the project was the development of a District Service Center for the SMEs of the Textile and Clothing sector. In particular, it investigates the introduction of innovative technologies to improve the process/product innovation of the sector. In this direction, the research unit proposal consisted in introducing document processing and indexing techniques on a variety (both for structure and content) of document formats whit the aim of improving the exchange of data among companies and the semantic content-based retrieval for the real companies’needs.

1

Introduction and Motivation

Since the seventies, the district represented one of the most common industrial model adopted from italian companies to face the crisis. It consists of an action of hiving-off (production decentralization) that relies on partitioning the work among companies in the same productive sector with specific professional abilities. This kind of model, however, rises the question about the interoperability among companies involved in it. Hence the need of improving communication standards and information sharing and exchanging. On the other hand, one of the key sector in the Made in Italy districts is the Textile and Clothing (TC) one as it represents about the 28,8% of the total industrial production. Thus it seems to be the perfect test-bed to start the investigation of a policy aimed at introducing innovative technologies in order to improve the process/product innovation of the sector. To this aim, the DDTA project (Apulia Region Project (2007-2010) - www. tessilpuglia.com) focuses on activities for the definition and diffusion of standards for interoperability in order to facilitate the cooperation and the collaboration among companies and for the development of ICT solutions specifically addressing the TC sector along with the development of a portal for delivering services to model and to support the District Service Center. In response to these challenges, the policy stated the following objectives: to facilitate SMEs access to systems of digital integration; to create a network among districts of Apulia Region to support the diffusion of management and M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 119–122, 2011. c Springer-Verlag Berlin Heidelberg 2011 

120

F. Esposito et al.

technological best practices in the usage of ICT and to implement already existing service centres in the district areas or to create new structures to supply companies with supporting services in the areas of process and product innovation, market intelligence, ICT usage. The achievements from this activity could made it possible to reach a clear and up-to-date idea of the state of the art and on the development trends relative to interoperability standards, IT solutions and initiatives in progress, bring the regions to adopt already existing and shared standards, maintain relations with other organizations for the definition of a national/international standard, promote the extension and improvement of the sector coding.

2

Scientific Challenges

The companies involved in a district must declare their skills in order to be selected for a specific work. Furthermore, in a more general business-oriented point of view, the companies have to be provided the right and accessible information about public announcements, import/export regulations, trade acts and so on. Exchanging data can be a challenge due to, among others, different specifications of formats and varieties of categorisations. If data is interpreted differently, collaboration is limited, takes longer and is not efficient. The primary criterion for interpreting documents is by content. Hence, the documents in the repository should be grouped and organized accordingly. However, doing this manually is very expensive, and doing it automatically is very difficult due to the need of capturing document meaning (i.e., semantics). A more tractable starting point is exploiting layout analysis (i.e., syntax). Indeed, probably significant content is often placed in particular layout components, so that being able to identify the typical components of each group allows to selectively read only those components for identifying the document content. As a consequence, the ability to handle and manage documents according to layout structure can be a key factor towards the ability to reach content-based management as well. However, the indexing process remains a key issue. A problem of most existing word-based retrieval systems consists of their ineffectiveness in finding interesting documents when the users do not use the same words by which the information they seek has been indexed. This is due to a number of tricky features that are typical of natural language. One of the most common concerns the fact that there are many ways to express a given concept (synonymy), and hence the terms in a users query might not match those of a document even if it could be very interesting for him. Another one is that many words have multiple meanings (polysemy), so that terms in a user’s query will literally match terms in documents that are not semantically interesting to the user. Moreover, in case of documents such as public announcements, import/export regulations and trade acts, the retrieval of relevant information becomes more difficult as the notification writer uses a technical terminology while the final user (companies’employee) doesn’t.

DDTA - Digitalisation of Districts in the Textile and Clothing Sector

3

121

Key Technologies

Organizing the documents on the grounds of the knowledge they contain is fundamental for being able to correctly access them. The key technologies to be exploited to reach this aim are: first-order incremental machine learning techniques for document layout processing, to classify documents and label their most significant components with the aim of representing the documents according to a standard format, and semantic indexing methodologies to guarantee an effective retrieval of relevant information according to a semantic level of the content. The problem of document layout processing in Digital Libraries environments requires a first-order language representation as the variety of documents without a common standard does not allow to represent the document with a format made up of a set of fixed components. Moreover, first-order setting can model and efficiently handle the relationships coming from the topological structure of all components in a document that result very useful in document labelling. Finally, the continuous flow of new and different documents calls for incremental abilities of the system that must be able to update or revise a faulty knowledge previously acquired for identifying the logical structure of a document. As for the indexing methodology, the weaknesses of term-matching based retrieval is overcome by Latent Semantic Indexing (LSI) technique whose basic idea is that there exists some underlying latent semantic structure in the data that is partially obscured by the randomness of word choice with respect to the retrieval phase and that can be estimated by means of statistical techniques. LSI relies on a mathematical technique called Singular-Value Decomposition (SVD). Starting from a matrix of term-document association data, the SVD allows to build and arrange a semantic space, where terms and documents that are closely associated are placed near each other, in such a way to reflect the major associative patterns in the data, and ignore the smaller, less important influences. The continuous flow of new documents that could be added to the initial database, requires an incremental methodology to update the initial LSI matrix. Two techniques have been developed in literature to update an existing LSI generated database: Folding In and SVD updating. The former uses the existing SVD to represent new information but yields poor-quality updated matrices, since the information contained in the new documents/terms is not exploited by the updated semantic space. The latter represents a trade-off between the former and the recomputation from scratch.

4

Contribution by the Research Group

The contribution of the research unit converges in the development of a document management system that intensively exploits intelligent techniques to support different tasks of document processing from acquisition to indexing, from categorization to storing and retrieval (1). A central role is played by the Learning Server, which intervenes during different processing steps in order to continuously adapt the knowledge taking into consideration new experimental evidence and changes in the context.

122

F. Esposito et al.

The layout analysis process on documents in digital format starts with a preprocessing module that rewrites basic PostScript operators to turn their drawing instructions into objects. It takes as input a digital document and produces the initial document’s XML basic representation, that describes it as a set of pages made up of basic blocks. Due to the large number of basic blocks discovered, an aggregation step is necessary. Since grouping techniques based on the mean distance between blocks proved unable to correctly handle the case of multicolumn documents, such a task was solved by exploiting a kernel-based method, implemented in the Learning Server, that is able to generate rewriting rules that suggest how to set some parameters in order to group together blocks to obtain lines. After that, a module collects the semantically related blocks into groups by identifying the surrounding frames based on white spaces and the results of the background structure analysis. At the end of this step, some blocks might not be correctly recognized, hence a phase of layout correction is automatically performed by exploiting embedded rules stored in the theories knowledge base. Such rules were automatically learned by a first-order incremental learning system implemented in the Learning Server from previous manual corrections collected on some document during the first trials. Once the layout structure has been correctly and definitely identified, a semantic role must be associated to each significant components in order to perform the extraction of the interesting text with the aim of improving document indexing. This step is performed by firstly associating the document to a class that expresses its type and then associating to every significant layout component a tag expressing its role. Both these steps use the theories previously learned and in case of failure the theories are properly updated by means of a first-order incremental learning system that runs on the new observations and tries to modify the old theories in the knowledge base. At the end of this step, both the original document and its XML representation, enriched with class information and components annotation, is stored in an internal document database. Finally, the text is extracted from the significant components and the Indexing Server, the module implementing the LSI techniques, is called to achieve a semantic indexing of document content useful for an effective content-based retrieval. Acknowledgements. The work was partially supported by the Regional Project “DDTA - Distretto Digitale a supporto della filiera produttiva del TessileAbbigliamento (2007-2010) - www. tessilpuglia.com ”.

Reference 1. Esposito, F., Ferilli, S., Basile, T.M.A., Di Mauro, N.: Machine learning for digital document processing: from layout analysis to metadata extraction. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. SCI, vol. 90, pp. 105–138. Springer, Heidelberg (2008) ISBN: 978-3-540-76279-9

DOMINUSplus - DOcument Management INtelligent Universal System (plus) Stefano Ferilli1 , Floriana Esposito1 , Teresa M.A. Basile1 , Domenico Redavid2 , and Incoronata Villani2 1

Computer Science Department, University of Bari “Aldo Moro” {ferilli,esposito,basile}@di.uniba.it 2 Artificial Brain S.r.l., Bari {redavid,villani}@abrain.it

Abstract. Activities of most organizations, and of universities in particular, involve the need to store, process and manage collections of different kinds of documents. Examples that require advanced solutions to such issues include the management of libraries, scientific conferences, research projects. DOMINUSplus is an open project born with the aim of harmonizing the Artificial Intelligence approaches developed at the LACAM laboratory with the research on Digital Libraries in a general software backbone for document processing and management, extensible with ad-hoc solutions for specific problems and context (such as universities).

1

Introduction and Motivation

The DOMINUSplus project springs from the interest of researchers in the LACAM laboratory of the University of Bari in applying Artificial Intelligence (and particularly Machine Learning) methodologies to Document Processing and Digital Library Management, as critical activities in many real-world domains. With specific reference to the context of universities, often practical problems do not facilitate the normal course of research activities, often due to the unavailability of digital material produced in the various activities. In many cases this depends on the lack of intelligent tools that can facilitate on one hand the inclusion of the material and on the other the retrieval. Some practical situations include: – Libraries. Manual cataloging may generate conceptual errors (e.g., an Operating Systems book might be catalogued as Artificial Intelligence); – Scientific conferences. Often, the reviewer assignment does not take into account the reviewer expertise (e.g., a paper concerning AI planning might be assigned to a reviewer from a different research field); – Research project documentation. Generally there is no common repository indexed for a department, faculty, or university that contains the documents produced under the various research projects in which the institution was involved, so finding information for the preparation of consistent and locally agglomerative project proposals is very complex. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 123–126, 2011. c Springer-Verlag Berlin Heidelberg 2011 

124

S. Ferilli et al.

This resulted in a general framework known as DOMINUS (Document Managemente Intelligen Universal System) (1) developed by the LACAM lab. The need for blending scientific research with technical expertise has led to the involvement of Artificial Brain company whose mission is to enhance local resources in the development of a knowledge-based economy according to the EU policies1 . The engineered artifact developed by Artificial Brain is DOMINUSplus .

2

Scientific Challenges

The project’s objective was to create a flexible and extensible framework to cover all aspects and functionality involved in the management of digital libraries, from the acquisition of new documents to the retrieval of documents considered as interesting for a given search query, focusing in particular on the semantics of the documents content. The DOMINUS framework processes digital documents through a pipeline consisting of several steps, aimed at acquiring increasingly abstract information from each incoming document, and specifically: – Acquisition. Documents in various digital formats are acquired and converted to a unified representation expressing both structure and content. – Layout Analysis. The various components that make up the structure of document pages are extracted and organized into a hierarchical structure. – Document Image Understanding. The kind/class of the document is identified and each component of the layout structure is associated with a label that expresses its logical role in the document (title, author, summary, etc.). – Text Analysis. The text in the relevant components is extracted and then processed using NLP techniques. – Categorization. The document is assigned to a category expressing its domain of interest. – Information Extraction. Additional relevant information is extracted from the document. Each of these steps poses specific research problems, most addressed and tackled using Artificial Intelligence techniques, several of which developed at LACAM.

3

Key Technologies

The role of Artificial Brain in the project was to engineer and implement the framework developed at LACAM for supporting different tasks according to current state-of-the-art technologies that could provide added value to the functionality of the single components, especially in the usability and scalability perspective. The DOMINUSplus architecture consists of several related components (see Figure 1), each designed to implement a particular functionality in the context of three specific tasks: Layout Analysis, Document Image Understanding and Document Understanding and Indexing. For document acquisition 1

The EC Community Strategic Guidelines on Cohesion 2007-2013 http://ec.europa.eu/regional policy/information/guidelines/ index en.cfm#1

DOMINUSplus

125

and layout analysis tasks, formats that are complaint to the Open Document Architecture and Interchange Format (ODA) standard (PDF, PS, ODT, etc..) have been considered. The Document Image Understanding task is carried out using the Inductive Logic Programming system INTHELEX, developed at LACAM, and transforming it into a Web service providing the following functionality: – Knowledge Base Management. It associates, to each user, a workspace organized in projects, in turn consisting of sets of theories, within which the user can create/import theories and make on them the subsequent operations. – Classification. Given an observation and a theory, it allows to calculate the confidence that the observation belongs to each concept defined in the theory. – Unification. Given two or more theories, it allows to create a unified theory containing all the concepts contained within these theories. – Refinement. Given a theory and a new example on which it fails, it allows to refine the theory so that it becomes consistent with all the previously observed examples and the current one. As to the Document Understanding and Indexing tasks, several established techniques and libraries were used throughout. For instance, in Text Recognition (e.g., GhostScript for PS/PDF documents and Tesseract for graphic components), Information Retrieval based on both classical term-based and advanced concept-based indexing techniques (including a Vector Space Model based on TF-IDF, Latent Semantic Indexing based on the log-entropy weighting scheme and Concept Indexing), Information Extraction (including several Keyword Extraction techniques based on different perspectives and Formal Concept Analysis for identifying interesting concepts). A full set of NLP techniques underlies all these tasks, including both standard (tokenization, language recognition, stopword removal, PoS tagging, stemming) and advanced ones (syntactic and semantic analysis, e.g., the Stanford parser, WordNet and WordNet Domains).

Fig. 1. DOMINUSplus Architecture

126

S. Ferilli et al.

Overall, the architecture makes extensive use of the object-relational mapping system called Hibernate, which enhances performances and allows the decoupling from a particular DBMS implementation.

4

Contribution by Research Group

Among the many techniques and approaches implemented in DOMINUSplus , the main contribution from the LACAM, representing the core of the framework and deserving more attention, is the logical learning tool. INTHELEX (Incremental Theory Learner from Examples) is an Inductive Logic Programming (ILP) system capable of learning hierarchical theories from positive and negative examples that adopts DatalogOI as a representation language. Its peculiarity consists in a fully incremental behavior (in addition to refining previously generated concept definitions, learning can also start from an empty theory). INTHELEX is able to learn simultaneously several (possibly inter-related) concepts/classes, ensuring the validity of theories learned in every moment. Specifically, it incorporates two inductive refinement operators for the revision of theories: one for generalizing definitions that reject positive examples, and the other for the specialization of definitions that explain negative examples. If a positive example is not covered, the system first attempts to generalize one of the available concept definitions referred to by the example, so that the resulting revised theory, covers the new example and is consistent with all previous negative examples. If a generalization of this type is found, it replaces the previous definition in the theory, or else a new clause is chosen for the calculation of the generalization. If the system cannot generalize any definition in a consistent manner, it attempts to add the negation of a condition in order to discriminate the negative example from all the previous positive. If this does not lead to results, the negative example is added to the theory as an exception, and each new observation will be compared with the exception before making inferences about theories. In addition to the inductive operators, INTHELEX is endowed with multistrategy reasoning capabilities based on Deduction (to identify information that is implicit in the observations), Abduction (to handle partial information) and Abstraction (to switch to more expressive description languages). Acknowledgements. DOMINUSplus is an open project promoted by LACAM (Laboratorio per l’Acquisizione della Conoscenza e l’Apprendimento nelle Macchine - http://lacam.di.uniba.it:8000/) and Artificial Brain S.r.l. (http://www.abrain.it/).

Reference 1. Ferilli, S.: Automatic Digital Document Processing and Management: Problems, Algorithms and Techniques. Springer Publishing Company, Incorporated (2011)

Europeana v1.0 Nicola Aloia, Cesare Concordia, and Carlo Meghini Institute for Information Science and Technologies of the Italian National Research Council (ISTI-CNR), Pisa {nicola.aloia,cesare.concordia,carlo.meghini}@isti.cnr.it

Abstract. The Europeana v1.0 is a Thematic Network project funded under the Commission’s eContentplus programme 2008 and is the successor network to the EDLnet thematic network that created the EDL Foundation and the Europeana prototype. The goal of the project is to develop an operational service and solve key operational issues related to the implementation and functioning of the European Digital Library. The work will include also a business development operation to ensure that a steady stream of content is made available in the Digital Library.

1

Introduction and Motivation

Europeana v1.0 is the successor network to the EDLnet thematic network which created the EDL Foundation and the Europeana prototype. The thematic network is coordinated by the EDL Foundation, which was formed to be responsible for the operation of the European Digital Library called Europeana [1]. It started in March 2009 and will end in July 2011. Following the launch of the prototype of Europeana, the EDL Foundation goal is to continue to develop the operational service of Europeana. This includes the development and implementation of all the necessary back end processes to run such an operation and a full-scale business development operation to ensure a steady stream of content is made available. Additionally dissemination efforts to end users are also executed to ensure take up and continuous involvement of end users in order to achieve and sustain such a service. An important effort has been also made in the development of services that allows others to re use and re purpose the data stored in the Europeana information space. The main objectives of the Europeana v1.0 are: – putting into practice the decisions on how to attract and maintain content into the service – building the back end systems needed to manage the delivery and access of this content – managing the channels enabling other environments to use the content made interoperable by Europeana, via web services or API’s – deploy the most usable levels of multilingual search and retrieval – disseminate the service to end users and provide tools to involve them actively. M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 127–129, 2011. c Springer-Verlag Berlin Heidelberg 2011 

128

N. Aloia, C. Concordia, and C. Meghini

The areas that this Thematic Network address are as follows: 1. Continued and proactive involvement of partners 2. Participation of new partners, particularly from publishing in the arts and humanities 3. Creation of license and partnership agreements 4. Rights management and IPR integration into Europeana the operational service 5. Management of partner content and continuous access to that content 6. Technical building of the service functional specification implementation 7. Hardware, storage and bandwidth connected with running the service 8. Service agreements with bodies involved in running the service 9. Implementation of new technologies 10. Implementation of new data and object models 11. Promotion of standards and data models 12. Dissemination to partners, stakeholders and end users 13. Distribution to other channels for mash ups and re-use 14. Policy development 15. Fund raising.

2

Scientific Challenges

A thematic area addressed by Europeana v1.0 that presents a major scientific challenge is the one concerning the conceptual modeling of the Europeana information space. Each of the different heritage sectors represented in Europeana uses different data models, some of which adhere to domain standards, while others are proprietary formats. This poses a very serious semantic interoperability problem. Moreover, Europeana is expected to exploit in the best possible way the information on cultural heritage objects provided by the contributing providers, in order to support sophisticate functionality, semantic search above all. The main challenge of the project is therefore to enable different formats to be interoperable at semantic level, while retaining the original expressivity and richness of the data. In order to meet this challenge, Europeana v1.0 has developed a data model, the Europeana Data Model (EDM, for short). The EDM is an attempt to transcend the respective information perspectives of the different sectors that are represented in Europeana the museums, archives, audiovisual collections and libraries. In particular, it makes a distinction between the intellectual and technical creation that is submitted by a provider (a bundle of resources about an object curated by the provider), the object this structure is about, and the digital representations of this object, which can be accessed over the web. Also, EDM adheres to the modelling principles that underpin the approach of the Web of Data (“Semantic Web”). EDM has been released for internal purposes on May 2011, and will soon be made public. Other important challenges in Europeana are tackling multilingualism and finding innovative ways of presenting tremendously large result sets (this is also referred to as info-graphics).

Europeana v1.0

3

129

Key Technologies

Technically speaking, Europeana is primarily perceived as a portal exposing search and browse functionalities enabling users to access a large collection of cultural heritage objects. Even though this perception is not entirely misleading, it does not capture the essential characteristics of what the Europeana really is: the Digital Library System (DLS) implementing Europeana is an open services platform enabling users and cultural institutions to access and manage the information space via an Application Program Interface (API). The Europeana DLS is built as a set of separate software components interacting through the Europeana API. The Europeana API enables Europeana developers to extend the DLS by adding new components, and third party developers to easily embed Europeana functionalities in their applications. To hide the complexity of the underlying system, the Europeana API are published as a set of callable methods, API endpoints and calling conventions. The actual implementation of the DLS components has been made using multiplatform languages and technologies (mainly Java related) and using the Web as underlying communication infrastructure. The Europeana foundation is currently organizing informal workshops to showcase the potential of the API usage for data providers, partners and endusers [2].

4

Contribution by Italian Research Community

The contribution of national research community to Europeana v1.0 relates to two main aspects. A major contribution has been done in defining the Europeana Data Model; the EDM is the result of the work of an international and multi disciplinary team of researchers leaded by the ISTI-CNR team, who has been responsible for editing the specification of the model, obtained through a complex process, involving a team core experts and a community with hundreds of members. Another important contribution has been the implementation at ISTI- CNR of a development infrastructure called Europeana Labs. The Europeana Labs is a set of servers that are used by the community of developers working in Europeana or in other related projects in order to test or showcase to a limited audience their applications or tools. The Europeana Labs also hosts the servers used for the cooperative working. 1. http://europeana.eu/ 2. http://www.europeana-libraries.eu/web/api/ hackathons

EuropeanaConnect Franco Crivellari, Graziano Deambrosis, Giorgio Maria Di Nunzio, Marco Dussin, and Nicola Ferro Department of Information Engineering, University of Padua {crive,deambros,dinunzio,dussinma,ferro}@dei.unipd.it

Abstract. EuropeanaConnect delivers core components which are essential for the realisation of the European Digital Library (Europeana) as a truly interoperable, multilingual and user-oriented service for all European citizens.

1

Introduction and Motivation

EuropeanaConnect1 solves key operational issues related to the implementation and functioning of Europeana. Furthermore, stakeholders, including users, will continue to be involved in creation of the operational Europeana and informed how they can contribute and access content in building and maintaining the operational service. EuropeanaConnect delivers highly scalable, crucial infrastructure components which will enable Europeana to integrate, manage, make interoperable, persist and make available via innovative access channels a vast amount of digital content from libraries, museums, archives and audio-visual collections from across Europe. In addition EuropeanaConnect integrates key added-value services into Europeana which significantly enhance its usability and functionality. EuropeanaConnect is a best practice network, under the eContentPlus program (contract n. ECP-2008-DILI-528001), which lasts from may 2009 to October 2011 and involves more than 30 partners around Europe among research institutions, content providers, and industrial stakeholders.

2

Contribution by Italian Research Community

While the need for multilingual access to multinational and multicultural information systems is undoubted, few truly operational systems exist and can serve as examples. Several projects have done extensive studies on user needs and requirements for features for information system access, but few have paid specific attention to multilingual issues. EuropeanaConnect implements the solutions which are needed to provide true multilingual access to the content of Europeana. All content in Europeana is described and accessible through its metadata descriptions, which are standardised, but generally not multilingual. EuropeanaConnect will build translation 1

http://europeanaconnect.eu/

M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 130–131, 2011. c Springer-Verlag Berlin Heidelberg 2011 

EuropeanaConnect

131

modules that will process queries and surrogate descriptions (object data) and produce a suitable multilingual representation for the user. This will allow Europeana information and objects to be searched and presented independent of the language of both the searcher and the object descriptions. In other words EuropeanaConnect will enable users to find relevant content in Europeana even if it is not described in their native or preferred language. The development of multilingual functionalities has been accompanied by user studies and surveys [1] which investigated expectations and needs of the users with respect to multilinguality and query translation features, as well as their capabilities and experience in this settings. The development and deployment process receive constant feedback through user testing and evaluation. To this end, the developed components have been thoroughly evaluated by using the standard Ad-hoc TEL collections 2008 and 2009 [2,3], developed by the Cross-Language Evaluation Forum, and exploiting the DIRECT system for managing the experimental results [4], as made available by the PROMISE2 network of excellence [5].

References 1. Agosti, M., Crivellari, F., Deambrosis, G., Ferro, N., G¨ade, M., Petras, V., Stiller, J.: Deliverable D2.1.1 – Report on User Preferences for Multilingual Access in Europeana. EuropeanaConnect, EU eContentPlus, Contract N. ECP-2008-DILI-528001 (2009), http://www.europeanaconnect.eu/ 2. Agirre, E., Di Nunzio, G.M., Ferro, N., Mandl, T., Peters, C.: CLEF 2008: Ad Hoc Track Overview. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Pe˜ nas, A., Petras, V. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 15–37. Springer, Heidelberg (2009) 3. Ferro, N., Peters, C.: CLEF 2009 Ad Hoc Track Overview: TEL and Persian Tasks. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Pe˜ nas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 13–35. Springer, Heidelberg (2010) 4. Agosti, M., Ferro, N.: Towards an Evaluation Infrastructure for DL Performance Evaluation. In: Tsakonas, G., Papatheodorou, C. (eds.) Evaluation of Digital Libraries: An Insight into Useful Applications and Methods, pp. 93–120. Chandos Publishing, Oxford (2009) 5. Ferro, N.: PROMISE: Advancing the Evaluation of Multilingual and Multimedia Information Systems. ERCIM News 84, 49 (2011)

2

http://www.promise-noe.eu/

MBlab: Molecular Biodiversity Laboratory Corrado Loglisci, Annalisa Appice, Michelangelo Ceci, Donato Malerba, and Floriana Esposito Department of Computer Science, University of Bari “Aldo Moro” {loglisci,appice,ceci,malerba,esposito}@di.uniba.it

Abstract. Technologies in available biomedical repositories do not yet provide adequate mechanisms to support the understanding and analysis of the stored content. In this project we investigate this problem under different perspectives. Our contribution is the design of computational solutions for the analysis of biomedical documents and images. These integrate sophisticated technologies and innovative approaches of Information Extraction, Data Mining and Machine Learning to perform descriptive tasks of knowledge discovery from biomedical repositories.

1

Introduction and Motivation

The exponential increase in publication rate of new papers in biomedicine makes difficult for researchers to keep up with research progresses without the help of computational techniques. Over 16 million references to biomedical papers are currently contained in the Medline database, the main on-line repository of biomedical research literature. Textual data as Medline papers are generally unstructured and the available technologies do not provide adequate mechanisms for helping humans in deeply analyze very large amount of content. The need to analyze this volume of unstructured data has prompted the use of information extraction and data mining tools to automatically extract key biological information. Several methods have been presented so far. The approaches in information extraction have been more and more specialized and refined as much as to permit to identify and recognize facts of interest from text by considering both surface and deep information, such as keywords and syntactic structures. Data mining techniques are mainly used, as a layer of techniques on top of those of information extraction, to perform predictive (e.g., text categorization) and descriptive (e.g., association discovery) analysis. Moreover, the rapid expansion of biomedical image repositories raised problems that go beyond simple acquisition issues, and cause the need to organize and classify the contents in order to improve the effectiveness of the retrieval procedure. To this aim, the integration of machine learning and image processing techniques represents a suitable approach to face the task.

2

Scientific Challenges

In the project “MBlab - Molecular Biodiversity Laboratory” we have designed three computational solutions to support the activity of the researchers M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 132–135, 2011. c Springer-Verlag Berlin Heidelberg 2011 

MBlab: Molecular Biodiversity Laboratory

133

in biomedicine when documents are considered. Typically, the researchers need to recognize relevant information present in the documents (e.g., named entities as SNPs, gene names) and then formulate new hypothesis or infer new findings (e.g., identification of SNPs or gene names involved in particular pathologies). The proposed approaches perform three distinct descriptive data mining tasks and, in particular, permit to extract facts of interest from biomedical literature in large repositories and mine regularities based on statistical evidence from these facts of interest: regularity can denote a particularly well-established process which therefore can be biologically relevant. As for the biomedical image analysis, during the past years a considerable effort was spent in the definition of automatic tools for low level features extraction. However, they fail in dealing with one of the main characteristics of biomedical repositories represented by the existence of relations among the objects contained in the collection. Hence, the need of deriving relational high-level semantic annotations able to describe the objects in the collection in order to improve the indexing process.

3

Contribution by the Research Group

We explored the potentialities of the technologies and approaches of information extraction and data mining for three descriptive tasks of knowledge discovery in biomedical literature described in the following. Furthermore, we investigated the applicability of machine learning and image processing techniques for the extraction of meaningful annotations for effective indexing of biomedical images. Discovering Frequent Syntactic Structures. We study the problem of how to mine an unstructured biomedical text corpus in order to identify any syntactic structures of named entities which frequently appear in the documents retrieved from biomedical repositories on a specific topic. We propose a knowledge discovery framework which first annotates the named entities (Semantic Text Processor in Fig. 1) in the retrieved corpus and integrates these entities in syntactic structures (Syntactic Text Processor in Fig. 1). Then, it uses a multi-relational data mining approach for frequent pattern discovery to identify frequent syntactic structures in the form of subject-object(s)-verb (Frequent Pattern Discovery in Fig. 1). Discovered frequent patterns play the role of informative syntactic knowledge shared by the papers under study and provide an indication of the existence of interesting verbal based dependencies among named entities. Mining Temporal Links. Mining biomedical literature when considering the temporal dimension of the papers is another problem explored in the MBlab project. In particular, we focus on the identification of interesting and hidden relations (links) between seemingly unconnected entities when entities are cited or reported in scientific papers published in different periods of time. This provides us a means to unearth linkages which have not been discovered when observing the literature as static but which may have developed over time, when

134

C. Loglisci et al.

Fig. 1. Discovering Frequent Syntactic Structures from Biomedical Literature

considering its dynamic nature. The adopted computational solution first partitions the corpus based on distinct and consecutive time-intervals, then identifies the biomedical named entities present in each partition (Named Entity Recognition, Feature Selection in Fig. 2). Multiple-level association rules are mined from each time-interval (Association Rules Mining in Fig. 2), and finally, a process of chaining of association rules is performed through all sets of association rules, namely through the time-intervals, in order to link over time two input entities (Link Discovery in Fig. 2). Therefore, temporal links are discovered as chains of association rules and denote hidden relations between the entities in the rules. Pattern Discovery for Semantic Role Labeling. A bio-molecular event is a process that involves and transforms molecular entities. In the literature, these are reported and described as predicate-argument structures, where each argument corresponds to a single entity which plays a particular role in the described event. Our interest in the bio-molecular events is that of defining an approach able to label the roles of the entities present in the documents, or, in other words, to identify which entities are associated to the roles of bio-molecular events. The determination of the entities which more frequently play a specific role in a particular event may provide indications on the investigation to conduct on a specific biological process. The computational solution first identifies predicateargument structures (Pas Extractor, Frame Filling in Fig. 2) by exploiting a knowledge base on the semantic structure of the events (Event FRAMEs in Fig. 2). Then, the arguments of these structures are recognized as named entities (Named Entity Recognition in Fig. 2). This permits us to preserve the relational information of each event. Finally, a relational frequent pattern mining approach is used to discover the associations roles-entities and the relationships of these associations with the events (Frequent Pattern Discovery in Fig. 2).

MBlab: Molecular Biodiversity Laboratory

135

Fig. 2. Mining Temporal Links from Biomedical Literature (left) - Pattern Discovery for Semantic Role Labeling in Biomedical Literature (right)

Biomedical Image Annotation. In [3] we propose a suite of image processing and machine learning approaches to annotate biomedical microscope images with qualitative information rather then quantitative ones coming from low level features. In details, a set of morpho-structural features such as dimension, granularity and polarity of the cytoplasm of a cell are extracted. Generally, these morpho-structural features are manually analysed by the clinicians that use such information to assess the goodness of a cell for a specific therapeutic treatment. On this baseline process, aimed at extracting qualitative tags from an image, a first-order relational learning framework is designed to combine the qualitative information gathered from images with textual content about patients clinical data in order to learn relational models able to characterize the trend and the success of a therapeutic plan in indexing and retrieval processes. Acknowledgment. This work is in partial fulfillment of the research objectives of the project MBlab DM19410 “Molecular Biodiversity Laboratory”.

References 1. Appice, A., Ceci, M., Loglisci, C.: Discovering Informative Syntactic Relationships between Named Entities in Biomedical Literature. In: Proc. of International Conference on Advances in Databases, Knowledge, and Data Applications, DBKDA 2010, pp. 120–125 (2010) 2. Loglisci, C., Ceci, M.: Discovering Temporal Bisociations for Linking Concepts over Time. In: Proceedings of European Conference on Machine Learning and Principle and Practices of Knowledge Discovery in Databases, Athens, Greece (2011) 3. Basile, T.M.A., Esposito, F., Caponetti, L.: A Multi-relational Learning Approach for Knowledge Extraction in in Vitro Fertilization Domain. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 571–581. Springer, Heidelberg (2010)

A Personalized Intelligent Recommender and Annotator TEStbed for Text-Based Content Retrieval and Classification: The PIRATES Project Felice Ferrara and Carlo Tasso Department of Mathematics and Computer Science, University of Udine {felice.ferrara,carlo.tasso}@uniud.it

Abstract. This paper presents the PIRATES (Personalized Intelligent Recommender and Annotator TEStbed for text-based content retrieval and classification) Project. This project faces the information overload problem by taking into account semantic and social issues: an integrated set of tools allow the users to customize and personalize the way they retrieve, filter, and organize Web resources.

1

Introduction and Motivation

The tremendous volume of digital contents available on the Web generates the information overload problem: the task of filtering new contents appropriate to individual needs is hard. Moreover, the growing amount of user generated contents published by means of Web 2.0 environments (such as forums, blogs, and social networks) makes this problem even harder. Obviously, an effective classification of the Web resources can tackle this problem: such classification can be used to filter the resources which match the user interests. For this reason, a core function in a framework aimed at facing information overload is the classification module: more accurate is the classification of resources and more precise is the description of the user interests and better filtering performance can be obtained. However, such classification cannot be exploited by means of a manual activity (such as extracting small portions of relevant information from available contents, or classifying contents according to a specific model of user interests) due to the large amount of resources to be considered. In order to overcome this limitation, Semantic Web and adaptive personalization technologies have been proposed: the classification and matching processes do not involve human intervention since a semantic layer is automatically added in order to classify resources. Ontologies are one of the main Semantic Web tools able to associate a clear semantic to Web resources. However, ontologies are domain dependent and, for this reason, it is quite difficult to integrate these technologies in domain independent frameworks. On the other hand, in social tagging systems the classification task is performed by users: each user of a Web 2.0 site can freely choose a set of terms, called tags, in order to classify Web M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 136–139, 2011. c Springer-Verlag Berlin Heidelberg 2011 

A Personalized Intelligent Recommender and Annotator TEStbed

137

resources. Obviously, such a domain independent approach cannot be rigorous since users do not have to respect specific rules and consequently tags often do not have a clear semantic meaning. So shortcomings of Semantic Web technologies can be faced by using socially defined classifications, and vice versa, user generated classifications can be supported by Semantic Web tools in order to produce more meaningful classifications. Following this vision, we have proposed an experimental testbed (called PIRATES) to merge social and semantic technologies in order to enhance the access to the available knowledge available on the Web. More specifically the PIRATES integrates several tools in order to support the users in the following tasks: – Classifying resources. Given an input document, several strategies are used to capture its meaning: extracting keyphrases from the specific document, browsing ontologies in order to find more abstract relevant concepts, and exploiting the collective intelligence provided by Web 2.0 users. – Finding relevant contents and people. Web resources are crawled (by a set of software agents) and classified by adaptively taking into account the specific user interests. Moreover, in order to identify relevant resources for a specific information need, both content-based approaches and collaborative filtering strategies are exploited. PIRATES is a general framework aimed at providing support in many different scenarios: in PIM (Personal Information Management), for supporting the identification of relevant Web contents in a personalized way; in E-Learning for supporting the tutor and teacher activities for monitoring (in a personalized fashion) student performance, behavior, and participation; in knowledge management contexts (including for example scholarly publication repositories and, more in general, digital libraries) for supporting document filtering and classification and for alerting users in a personalized way about new posts or document uploads relevant to their individual interests; in online marketing for monitoring and analyzing the blogosphere where word-of-mouth and viral marketing are nowadays more and more expanding and where consumer opinions can be listen.

2

The PIRATES Framework

Figure 1 shows the general architecture of the PIRATES framework. A set of software agents are used for crawling Web resources as well as other meaningful information provided by Web 2.0 users. Web resources are then classified/labeled by means of a set of tools: – the IEM (Information Extraction Module), based on the GATE platform, extracts named entities, adjectives, proper names, etc. from input documents; – the KPEM (Key-Phrases Extraction Module) [1] extracts meaningful keyphrases which summarize each input document;

138

F. Ferrara and C. Tasso

Fig. 1. The general architecture of PIRATES

A Personalized Intelligent Recommender and Annotator TEStbed

139

– the IFT (Information Filtering Tool) [2] evaluates the relevance (in the sense of topicality) of a document according to a specific personalized model of user interests represented with semantic (co-occurrence) networks; – the STE (Social Tagger Engine) suggests new annotations for a document relying on the tags generated by Web 2.0 users: social applications (such as delicious, BibSonomy, etc.) are also monitored in order to model the behavior of Web 2.0 users. The personal interests of each user are inferred by taking into account the set of resources that he/she tagged [3]. – the ORE (Ontology Reasoner Engine) [1] suggests more abstract concepts by browsing through ontologies, classification schemata, thesauri, lexicon (such as WordNet) and by using information extracted by the IEM, KPEM, IFT, and STE modules. – the SAT (Sentiment Analysis Tool) [4] is a specific plug-in for personalized sentiment analysis that is capable of mining consumer opinions in the blogosphere; PIRATES is also capable to recommend new potentially relevant contents and to identify people with interests similar to the user. For this purpose, PIRATES includes: – the Resource Recommender module which filters resources according to an analysis of tags and resources considered by the users; – the People Recommender module, which identifies people which share specific interests with the user.

3

Conclusions

The development of PIRATES is ongoing and has been planned in an incremental fashion, interleaved with several experimental evaluation phases. In fact, several modules has been already developed, integrated, and tested and the first experiments show that the proposed framework is a promising approach to automatic, personalized classification of Web contents.

References 1. Pudota, N., Dattolo, A., Baruzzo, A., Ferrara, F., Tasso, C.: Automatic keyphrase extraction and ontology mining for content-based tag recommendation. International Journal of Intelligent Systems, Special Issue: New Trends for Ontology-Based Knowledge Discovery 25, 1158–1186 (2010) 2. Minio, M., Tasso, C.: User modeling for information filtering on internet services: Exploiting an extended version of the umt shell. In: 5th UM Inter. Conf. UM for Information Filtering on the WWW (1996) 3. Ferrara, F., Tasso, C.: Extracting and Exploiting Topics of Interests from Social Tagging Systems. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS, vol. 6943, pp. 285–296. Springer, Heidelberg (2011) 4. Casoto, P., Dattolo, A., Tasso, C.: Sentiment classification for the italian language: A case study on movie reviews. Journal of Internet Technology 9, 365–373 (2008)

PROMISE – Participative Research labOratory for Multimedia and Multilingual Information Systems Evaluation Emanuele Di Buccio, Marco Dussin, Nicola Ferro, Ivano Masiero, and Gianmaria Silvello Department of Information Engineering, University of Padua {dibuccio,dussinma,ferro,masieroi,silvello}@dei.unipd.it

Abstract. Measuring is a key to scientific progress. This is particularly true for research concerning complex systems, whether natural or human-built. PROMISE will provide a virtual laboratory for conducting participative research and experimentation to carry out, advance and bring automation into the evaluation and benchmarking of complex multilingual and multimedia information systems.

1

Introduction and Motivation

With a population of over 500 million in its 27 states in which more than 80 indigenous and many more immigrant languages are found, the citizens and companies of the EU demand information systems that allow them to interact with the culturally and politically diverse content that surrounds them in multiple media. Moreover, with the advance of broadband access and the evolution of both wired and wireless connection modes, users are now not only information consumers, but also information producers: language and media barriers are no longer seen as inviolable and they are constantly crossed and mixed to provide content that can be accessed on a global scale within a multicultural and multilingual setting. The technology and research behind multilingual and multimedia information systems are, today, in the position of intercepting these emerging trends but their design and development is becoming increasingly complex and needs proper means for ensuring that they meet the expected user requirements and provide the desired effectiveness.

2

Scientific Challenges

We consider experimental evaluation both laboratory and interactive a key means for supporting and fostering the development of multilingual and multimedia information systems which are more adherent to the new user needs in order to ensure that they meet the expected user requirements, provide the M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 140–143, 2011. c Springer-Verlag Berlin Heidelberg 2011 

PROMISE

141

desired effectiveness and efficiency, guarantee the required robustness and reliability, and operate with the necessary scalability. PROMISE1 (Participative Research labOratory for Multimedia and Multilingual Information Systems Evaluation) aims at advancing the experimental evaluation of complex multimedia and multilingual information systems in order to support individuals, commercial entities, and communities who design, develop, employ, and improve such complex systems. The overall goal of PROMISE is to deliver a unified environment collecting data, knowledge, tools, methodologies, and the user community which are involved in the experimental evaluation. PROMISE is a network of excellence (contract n. 258191), funded in the 7th Framework Programme of the European Commission, with 10 academic and industrial partners, who are leaders in the field: University of Padua, Italy (coordinator); Swedish Institute of Computer Science (SICS), Sweden; University of Amsterdam, The Netherlands; Sapienza University of Rome, Italy; University of Applied Sciences Western Switzerland, Switzerland; Vienna University of Technology, Austria; Zurich University of Applied Sciences, Switzerland; HumboldtUniversitt zu Berlin, Germany; Evaluations and Language resources Distribution Agency (ELDA), France; Centre for the Evaluation of Language Communication Technologies (CELCT), Italy. The PROMISE project lasts three years, starting from September 2010 and ending in August 2013. To achieve its goals, PROMISE will pursue the following activities: – Foster the adoption of regular and thorough experimental evaluation activities: it will carry on the successful and renowned CLEF2 (Cross-Language Evaluation Forum) evaluation campaigns further pushing the evaluation exercises to tackle realistic tasks, use cases, and data sets; – Bring automation into the experimental evaluation process: it will propose methods and provide software infrastructure to create larger experimental collections; increase the number and size of the experiments conducted; and develop distributed, asynchronous, and loosely-coupled evaluation protocols, moving experimental evaluation from handicraft process to a mostly automatic one. – Promote collaboration and re-use over the acquired knowledgebase: it will curate, preserve, and enrich the collected experimental data; provide the means for an easy comparison with and a meaningful interpretation and visualisation of the experimental results; and facilitate the discussion and collaboration among all the interested stakeholders. – Stimulate knowledge transfer and uptake: PROMISE will disseminate know-how, tools, and best practices about multilingual and multimedia information systems; facilitate uptake and participation by commercial entities and industries; and give rise to multidisciplinary competencies and expertises. 1 2

http://www.promise-noe.eu/ http://www.clef-campaign.org/

142

3

E. Di Buccio et al.

Key Technologies and Contribution by Italian Research Community

Large-scale evaluation initiatives, such as Text REtrieval Conference (TREC) in the United States, the CLEF in Europe, and the NII-NACSIS Test Collection for IR Systems (NTCIR) in Asia, contribute significantly to advancements in research and industrial innovation in the information retrieval sector, and to the building of strong research communities. A study conducted by NIST reports that “for every $1 that NIST and its partners invested in TREC, at least $3.35 to $5.07 in benefits accrued to IR researchers. The internal rate of return (IRR) was estimated to be over 250% for extrapolated benefits and over 130% for unextrapolated benefits”. Large-scale evaluation campaigns produce a huge amount of extremely valuable scientific data which provides the foundations for subsequent scientific production and system development and constitutes an essential reference for literature in the field. This data is also economically valuable, due the considerable effort devoted to its production: the NIST study estimates in about 30 million dollars the overall investment in TREC. Nevertheless, little attention has been paid over the years to modelling, managing, curating and accessing the scientific data produced by evaluation initiatives, despite the fact that the importance of scientific data in general has been highlighted by many institutional organizations, such the European Commission, the US National Scientific Board, and the Australian Working Group on Data for Science. Our goal is to deliver a unified infrastructure and environment for data, knowledge, tools, methodologies, and the user community in order to advance the experimental evaluation of complex multimedia and multilingual information systems[1]. The evaluation infrastructure will: – manage and provide access to the scientific data produced during evaluation activities; – support the organization of evaluation campaigns; – increase the automation of the evaluation process; – provide component-based evaluation; – foster the usage and understanding of the scientific data; A user-centered design approach will be adopted involving the different stakeholders, e.g. scientists, evaluation campaign organizers, system developers, students, in the development of the infrastructure. The evaluation infrastructure is not limited only at managing the experimental results but aims also at exploring how we can improve the comprehension of and the interaction with the experimental results by researchers and system developers. We imagine the following scenarios: (i) a researcher or a developer is attending the workshop of one of the large-scale evaluation campaigns and s/he wants to explore and understand the experimental results as s/he is listening at the presentation discussing them; (ii) a team of researchers or developers is working on tuning and improving a system and they need tools and applications

PROMISE

143

Fig. 1. Prototye iPad application for interactively exploring the experimental results of an information access system

that allow them to investigate and discuss the performances of the system under examination in a handy and effective way. To this end, we are investigating the adoption of innovative devices, such as the iPad, which can allow for a natural and easy interaction with the experimental results and the scientific data [2,3], as shown in Figure 1.

References 1. Ferro, N., Hanbury, A., M¨ uller, H., Santucci, G.: Harnessing the Scientific Data Produced by the Experimental Evaluation of Search Engines and Information Access Systems. Procedia Computer Science 4, 740–749 (2011) 2. Di Buccio, E., Dussin, M., Ferro, N., Masiero, I., Santucci, G., Tino, G.: Interactive Analysis and Exploration of Experimental Evaluation Results. In: Wilson, M.L., Russell-Rose, T., Larsen, B., Kalbach, J. (eds.) Proc. 1st European Workshop on Human-Computer Interaction and Information Retrieval (EuroHCIR 2011), pp. 11–14 (2011), http://ceur-ws.org/Vol-763/ 3. Ferro, N., Sabetta, A., Santucci, G., Tino, G.: Visual Comparison of Ranked Result Cumulated Gains. In: Miksch, S., Santucci, G. (eds.) Proc. 2nd International Workshop on Visual Analytics (EuroVA 2011). Eurographics Association, Geneve (2011)

Cooperative Digital Asset Management in the Scientific Field: Strategies, Policies, Interoperability and Persistent Identifiers Maurizio Lancia1 , Roberto Puccinelli1 , Massimiliano Saccone2 , Marco Spasiano1 , and Luciana Trufelli2 1

2

Office for Information Systems, Italian National Research Council, Piazzale Aldo Moro, 7 – 00185 Rome, Italy {maurizio.lancia,roberto.puccinelli, massimiliano.saccone,marco.spasiano}@cnr.it Central Library “G. Marconi”, Italian National Research Council, Piazzale Aldo Moro, 7 – 00185 Rome, Italy {luciana.trufelli}@cnr.it

Abstract. In this paper we present a series of activities carried out within the National Research Council of Italy (CNR) and aimed at the development of a unique, certified and open archive of CNR’s digital research products. Starting from the description of CNR’s distributed library system, we then briefly talk about CNR’s involvement in OA initiatives and the role played by CNR’s Information System Office in providing technological tools for digital asset management. Afterwards we try to point out some criticalities of OA archives. We then talk about the solution we propose for the development of a unique, certified and open archive using a cooperative approach that takes into account previous experiences, existing repositories, policy and organizational issues. We also present the processes we designed for content ingestion and validation and the strategies for persistent identification. We finally illustrate the technical solutions we have developed as prototype proposals for the community.

1

Introduction

The use of digital libraries in research institutions presents some peculiarities. Besides the necessity of effective digital resource management, this type of organizations need to design and deploy effective methods and tools to tackle complex problems of research product quality assessment and overall performance measurement. Scientific production statistics and bibliometric indicators are widely leveraged in research evaluation processes. Although criticisms have been raised by the scientific community especially against the latter, the Italian legislation has recently confirmed their use [1]. In order to address the challenges of a wide and diffuse access to scientific production and to improve the reliability of quantitative and qualitative measures of M. Agosti et al. (Eds.): IRCDL 2011, CCIS 249, pp. 144–159, 2011. c Springer-Verlag Berlin Heidelberg 2011 

Cooperative Digital Asset Management in the Scientific Field

145

research production, this paper proposes an approach based on the cooperation of all the stakeholders working in the scientific environment. We give particular emphasis to the peculiarities of OA repositories, which, in our opinion, should be the back-bone of the scientific community knowledge base. We think that this approach could favor the innovation of the system of scientific libraries in Italy. The increased accessibility of digital resources provided by the web technologies and the development of the information systems demand an increased effort for resource certification and contextualization (i.e. keeping the relations between digital resources and their respective contexts). The quantity and complexity of the problems posed by a multi-institutional environment, require organizational and political measures in first place. Technical solutions will follow, provided that all the adopted standards and technologies will have to be open. Within CNR1 Central Administration we are working to design and implement prototype solutions to the problems described above, in order to provide practical inputs to the discussion both at the National and International level. In order to achieve our goal, we look for cooperation and inputs from all CNR’s scientific network and from external organizations playing key roles in the research field. The results of our work may represent a starting point for future cooperative developments and are made available to the scientific community in order to ensure the maximum consensus about the choices to be made.

2

CNR’s Environment

2.1

The Organization of CNR’s Libraries

CNR’s library infrastructure reflects CNR’s organization, featuring a Central Administration in Rome and a Scientific network made up of thematic institutes distributed all over the national territory. A significant percentage of CNR’s institutes are hosted inside territorial Research Areas, which provide common services thus increasing efficiency. CNR’s library system features a hierarchical and distributed organization, which includes a Central Library (Biblioteca Centrale), Research Area Libraries (Biblioteche delle Aree di Ricerca), Institute Libraries (about 80). It can provide a wide range of services to the entire scientific community and has recently adopted new organizational measures in order to increase the coordination of its different branches and improve the quality of the services provided to the internal scientific community. This effort has already produced some results in terms of process rationalization and digital resource sharing. The medium term objective is to complete the integration between CNR’s libraries and to provide new added value services both to the internal and external scientific community. 1

The National Research Council of Italy is a public body whose mission is to carry out and promote research activities in the main sectors of knowledge and to disseminate the results and their applications in order to foster the scientific, technological, economic and social development of the Country (Legislative decree n. 127, 4th of June 2003).

146

2.2

M. Lancia et al.

CNR’s Libraries and OAI Initiatives

In the recent years CNR’s libraries have been involved in numerous Open Access initiatives, in the belief that these can help tackling some problems like the decrease of available economic resources; the continuous evolution of technological tools; the issues regarding long term digital preservation; the great amount of inadequately identified and certified digital resources; the unfavorable price policies of some commercial services. Open Access initiatives are by necessity implemented through the tight cooperation between the CNR’s libraries and the CNR’s organizational units which provide ICT services. A first outcome of this cooperation is the development of some OA repositories for CNR’s research products. Those repositories can contain several types of research products (publications, data sets, patents, grey literature, etc). At present, CNR’s libraries are contributing to the implementation of an integrated system connecting all CNR’s OA research product archives and able to overcome some of the problems described in the previous sections (e.g. interoperability with the main information and knowledge management systems, transparent access to the entire CNR’s scientific production, quality issues, persistent and unique identification of digital resources, unified digital preservation policies, etc.). 2.3

The Role of CNR’s Information Systems

The management of digital archives within CNR’s Central Administration is carried out through a tight cooperation between the Central Library and the Information System Office. This collaboration has been recently boosted in order to achieve a higher integration between the digital libraries and the other components of CNR’s information system, which include the ERP system, the business intelligence platform, web portals and informational web sites. Figure 1 shows the new integrated architecture of CNR’s information systems. At the bottom there are the so called Enterprise Information Systems (EIS), including the ERP data bases, administrative document bases and the scientific digital libraries. At the upper level we find the applications which access and manage the data kept at the EIS level. At the top of this architecture there is the level of access portals and informational web sites, which provide different interfaces to the different types of users. Orthogonal to this layered architecture is a set of infrastructural services for unified authentication and authorization, digital signature, certified e-mail, persistent identifier assignment and resolution, etc. The integration of scientific digital libraries with the other information systems can provide many benefits in terms of increased accessibility and can also allow for a more effective evaluation process. Actually, the possibility of linking the research products with administrative information regarding personnel, projects and financial accounting is a pre-requisite for evaluating the performance at different levels (single researchers, research groups, institutes, departments, organization).

Cooperative Digital Asset Management in the Scientific Field

147

New CNR IS architecture Access layer

Infrastructural services

Application portal

Institutional web site

Authentication (LDAP)

Informational web sites

Application service layer Authorization

ERP Accounting

Workflow engine

SSO

Business Intelligence

Personnel service




CNR Data

SIPER




Research

Warehouse HR

Data base and archive layer

Digital signature

Administrative data bases Digital certified mail

Administrative archive

Research

Accounting

Documents Persistent identifier register

Institutional archive of scientific production

Articles Technical reports

HR

Protocol

....

Fig. 1. The new CNR’s Information System Architecture

In order to achieve the desired integration of digital libraries within CNR’s information system it is crucial to take into account the existing situation, which features multiple and distributed repositories. We think that the best approach is to federate the archives by introducing a series of common policies which define metadata sets, authority files, data interchange protocols, document ingestion and management processes. Obviously this change have to be introduced by gathering inputs and consensus from CNR’s scientific network. Needless to say that all the technical solutions have to be based on open standards and open source platforms. With respect to this point, it is worth mentioning that CNR’s information systems have started more than ten years ago a process of migration towards open source solutions, shifting the investments from commercial products to highly skilled personnel. A software factory has been established some years ago inside the Information System Office, which develops and maintain the majority of the components of CNR’s information system. This, in our opinion, helps in tailoring solutions to the specific needs of our organization.

3

Criticalities of OA Archives

In recent years the main scientific institutions and the stakeholders of the scientific publishing sector have spent a significant effort in enhancing and improving

148

M. Lancia et al.

the quality of OA initiatives and of the annexed services. Among the many initiatives in this field we would like to mention: the development of aggregated services for OA repositories, managed by numerous institutional and disciplinary service providers; the significant stimulus provided at the international level by the European Commission2 and by the European Research Council (ERC) [2, 3] and, at the Italian national level, by the Italian Conference of University Rectors (Conferenza dei Rettori delle Universit Italiane – CRUI) [4] to the deployment of organizational and technological infrastructures for the collection of and access to research papers funded by public institutions and for their deposit in institutional and/or disciplinary archives3. Although much effort has been spent to overcome the criticalities of OA archives, some issues still need to be addressed. Among these we would like to mention: – interoperability at the policy, organizational and technological level, especially with respect to national and international administrative data management systems and to some of the most important public and commercial bibliographic data bases (e.g. OECD, European Commision data banks, Anagrafe Nazionale dei Prodotti della Ricerca, Web of science, Scopus, etc); – extensive coverage of the institutional scientific production; – quality control as regards the scientific contents of the deposited items4 ; – unique and persistent identification of works and authors; – quality control and certification of the descriptive metadata (bibliographic and authority control). – version history tracking. In general, some of the main problems affecting OA repositories are caused by or connected to the fragmentation of OA initiatives, which, at the Italian national level, is combined with the lack of common strategies and policies. This sometimes leads to duplicated efforts [5]. All those problems hinder the use of 2

3

4

European Commission, Open Access Pilot in FP7, European Communities Publication Office, August 2008. The 12th of December 2008 the European Commission launched the “Open Access Pilot in FP7”, with the aim of defining innovative strategies and tools that could guarantee open access to data and publications coming from EC funded research projects. The EC provided recommendations to the R&D institutions about the adoption of mandatory policies regarding the deposit in disciplinary or institutional OA archives. A Digital Agenda for Europe – Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Brussels, 26.8.2010 - COM(2010) 245 final/2. http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=CELEX:52010DC0245(01):EN:NOT It is also worth mentioning, among the EU projects in which CNR is involved: DRIVER – Digital Repository Infrastructure Vision for European Research – http://www.driver-repository.eu/; OpenAIRE – Open Access Infrastructure Research for Europe – http://www.openaire.eu/ The institutional repositories are not always able to certify the quality of unpublished works, neither are they designed for easy integration with open peer-review services.

Cooperative Digital Asset Management in the Scientific Field

149

institutional repositories as reliable and certified tools for statistics collection and research evaluation [6]. In our opinion, the main causes of the problems listed above are: – insufficient allocation of economic and human resources, which raise sustainability issues; – lack of mandatary policies for the deposit of the scientific production in OA institutional repositories; – problems in carrying out an effective advocacy of the scientific and technical communities of reference; – lack of cooperative systems for metadata control and certification; many OA repositories, even among those playing the role of institutional repositories, do not pursue the objective of providing bibliographic and authority metadata controls; this hinders or makes more difficult the retrieval and aggregation of research products and metadata (e.g. it is very difficult to retrieve the works of a single researcher if his/her name is recorded in different ways for different articles; in this case an author unique identifier and an author authority file would be of help). At present neither metadata standards5 nor commonly used digital library software favor the adoption of shared rules and conventions which would improve the quality of OA repositories [7,8,9]. – Lack of peer-review procedures for unpublished works [7]. – Lack of agreement between the sytakeholders about policies and strategies for the effective design, implementation and management of OA repositories. – Lack of added value services like: helpdesks tailored on the final users needs, effective DRM strategies. Despite the criticalities described above, many information professionals acknowledge the relevance of the Institutional Archives (IA), among all OA initiatives, as a strategic channel for an open, certified and immediate access to the products of universities and research institutions. According to the definition provided by Clifford A. Lynch [10], the Institutional Archive is as the set of services that a research organization offers to the members of its community for the management and dissemination of digital materials created by the institution and its community members. The types of managed digital materials must be indicated in an Open Access mandatory management policy, issued by the organization itself. The Institutional Archive is primarily an organizational commitment aimed at assuring: – the digital preservation of the materials mentioned above and of the related metadata; – the certified access to digital resources and metadata. The responsibilities regarding the management of this types of services may be assigned to different organizational units within an institution, provided that all 5

The adoption of commonly agreed metadata sets (Dublin Core, MARC, etc.) and the definition of highly refined mapping systems are not “per se” sufficient to guarantee the certified exchange and reuse of data and metadata.

150

M. Lancia et al.

those units cooperate within a coordinated and unified context, characterized by the collaboration between the stakeholders which take part in the decision processes: librarians, ICT professionals, archivists, user communities and policy makers.

4 4.1

An Open Archive for Research Products Institutional Objectives of CNR’s OA Repository and Metadata Quality Definition

Within the framework of the new integrated architecture of CNR’s information systems, a preliminary study have been carried out in order to verify the feasibility and the implications of an OA repository, which should collect, archive, catalogue, identify, certify and deliver CNR’s scientific production, with the aim of providing: a strategic support to CNR’s institutional mission, an increased visibility of CNR’s production, a quality certification of data and metadata, a certification tool able to support research evaluation processes [11]. In particular, great attention has been paid to descriptive and administrative metadata quality certification in order to guarantee: – unique and persistent bibliographic identification of the digital items and of their different versions as well as of the intellectual rights and authors’ affiliations; – visibility and impact maximization of the deposited works; – interoperability with internal catalogues and administrative data bases, bibliographic data bases, national and international statistics; – the reuse of metadata to support research evaluation processes; – organizational-technical interoperability with Italian and international service providers. The chosen approach is to define metadata quality by specifying their functions and usage contexts (functional approach): “Quality is very difficult to define. A definition that can be used in the context of metadata is: ’high quality metadata supports the functional requirements of the information and documentation system it is designed to support’, which can be summarized as ’quality is about fitness for purpose” [8]. “Quality metadata reflect the degree to which the metadata in question perform the core bibliographic and management functions of discovery, use, provenance, currency, authentication, and administration. The functional perspective is closely tied to the criteria and measurements used for assessing metadata quality.” In this case, “accuracy, completeness, and consistency are the basic criteria used in measuring metadata quality” [9]. In order to achieve accuracy, completeness and consistency of descriptive and administrative metadata, a content and metadata management system has been designed based on the OAIS conceptual model [12] and on a hierarchical and

Cooperative Digital Asset Management in the Scientific Field

151

distributed paradigm, which involves all CNR library system and the technical units of the Central Administration that provide knowledge management services. The goal is to tackle the issues described above, regarding accurate metadata quality control, persistent and unique identification, versioning, metadata contextualization and linkage with other CNR’s administrative data bases (personnel, projects, etc.), interoperability, etc. 4.2

Workflows for Digital Asset Ingestion and Validation

In this section we describe the process we designed for ingesting and verifying digital assets in our repository, which is summarized in Figure 2. In our opinion, this process suits large and distributed organizations with a distributed library system. The process includes three main workflows: archiving (which can be triggered by the author himself), first level verification (performed by institute and research area libraries – “local libraries” in what follows) and second level verification (which involves both the local libraries and the Central Library) [13]. In the self-archiving phase authors upload their documents along with a signed declaration in which they state their authorship rights and fill in a form for descriptive metadata. They can also ask for a peer review of the contents, which will be carried out by appointed reviewers. The same steps can be performed by the local libraries on behalf of the authors.

Fig. 2. Archiving Workflow

152

M. Lancia et al.

In the first level verification phase (Figure 3) the local libraries check metadata for completeness and correctness. They can interact with authors in case modifications are needed.

Fig. 3. First level verification

In the second level verification phase (Figure 4) the Central Library performs a second check on metadata, interacting with the authors and the local library for any required clarification/modification. The final part of this workflow includes filling in some metadata fields that are specific to the Central Library and performing the final steps for validation, certification and publication. 4.3

Metadata Certification: Policies, Organization and Tools

As regards authentication, ingestion, metadata verification/integration, final validation and publishing phases, the system allows for a constant feed-back between users and system managers, by means of an helpdesk service, which support the user in all the steps of the deposit procedure. The system features three different workflows: a) deposit accomplished by one of the authors – i.e. self archiving; b) deposit accomplished by the local library of the institute to which the authors belong;

Cooperative Digital Asset Management in the Scientific Field

153

Fig. 4. Second level verification

c) deposit accomplished by the Central Library (generally for the Organizational Units of the Central Administration or for those without a local library). In all three cases the digital material undergo a thorough review and validation process (bibliographic control) that can be performed at various levels (Institute, Research Area, Central Library). We have also planned to trigger a cooperative development of authority control tools as regards, for instance, intellectual responsibility (authors, editors, etc.), periodical journals and series titles (linked to the ISSN International Centre data base), etc. 4.4

Interoperability with External Information and Knowledge Management Systems: A Cooperative Approach

From a technical view point, though OAI-PMH protocol allows – as a container – to embed different types of metadata formats, it limits interoperability to the elements of the unqualified Dublin Core schema. If this on one hand simplifies metadata management on the provider’s side, on the other hand it increases the “noise” in research products evaluation [7, 9]. In order to guarantee an actual interoperability at the service provider level, it would be necessary to develop cooperative bibliographic and authority control systems for the main shared bibliographic elements (author, contributor, title,

154

M. Lancia et al.

subject, type, relation, etc.) and adopt, in a cooperative fashion, persistent identification systems for intellectual responsibilities, types of resources and different versions of the same objects. In our opinion, in order to pursue metadata quality, the cooperative approach should take into account functional requisites of the national and international information systems to which they provide support, thus assuring: – persistent and unique identification of the deposited works and of their different versions as well as of the authors’ affiliations; – visibility and impact maximization for the deposited items; – interoperability between bibliographic, statistical and administrative data bases, both at the national and international level; – technical and organizational interoperability between Italian service providers. 4.5

Persistent Identification

Persistent identifiers (PI) are alphanumeric codes that allow to uniquely and persistently identify digital resources. Besides, they are also a pre-requisite for the certification of authenticity, provenance and managing rights. There are currently several PI standards, which present different features and suit the needs of different communities. It is highly probable that the situation will not change in the future, i.e. there won’t be a unique PI technology or only one central digital resource registry for the entire world [13]. Within this section we provide a short list of the most diffused PI standards. The Document Object Identifier system (DOI [14]) is a business-oriented solution widely adopted by the publishing industry, which provides administrative tools and a Digital Right Management System (DRM). Archival Resource Key (ARK [15,16]) is an URL-based persistent identification standard, which provides peculiar functionalities that are not featured by the other PI schemata, e.g., the capability of separating the univocal identifier assigned to a resource from the potentially multiple addresses that may act as a proxy to the final resource. The Handle System [17, 18, 19] is a technology specification for assigning, managing, and resolving persistent identifiers for digital objects and other resources on the Internet. The protocols specified enable a distributed computer system to store identifiers (names, or handles) of digital resources and resolve those handles into the information necessary to locate, access, and otherwise make use of the resources. Finally, the Persistent URL (PURL [20]) is simply a redirect-table of URLs and it’s up to the system-manager to implement policies for authenticity, rights, trustability, while the Library of Congress Control Number (LCCN [21]) is a persistent identifier system with an associated permanent URL service (the LCCN permanent service), which is similar to PURL but with a reliable policy regarding identifier trustability and stability. In the Library domain the National Bibliography Number has been defined and is currently promoted by the CENL. IETF RFC 3188 [22, 23] describes the

Cooperative Digital Asset Management in the Scientific Field

155

use of URNs to represent NBN persistent identifiers. The NBN standard assumes that the national libraries are responsible for the national name registers. The first implementations of NBN registers in Europe are available at the German and Swedish national libraries. We have chosen to use NBN codes to identify the resources used in our testing activities, because this is an open standard, whose URN-based format well maps a distributed and hierarchical organization of digital libraries like CNR’s one. To this end in the recent years we have participated to some initiatives regarding persistent identification, in order to identify and implement appropriate solutions for the research environment. The NBN syntax is quite straightforward. According to RFC 3188 an URNNBN PI which uses ISO 3166 codes to identify countries has the general form: URN:NBN:: {<sub-namespace code>}– where the assigned NBN string can be any sequence of alphanumeric characters. This syntax allows to split the national domains in any number of sub-domains, hierarchically organized. The effectiveness of any type of PI is more a policy issue than a technical one. In the case of NBN, policies are mainly established at the national level and require the cooperation and the agreement of all the main actors in the field of digital preservation [24]. In Italy an initiative for establishing a national infrastructure for NBN assignment and resolution is being carried out, involving the National Library of Florence (BNCF), the National Library of Rome (BNCR), the Central Institute for Unique Catalogue (ICCU), the National Research Council of Italy (CNR), Fondazione Rinascimento Digitale (FRD) and University of Milano (UniMi). The proposed model for the Italian infrastructure is based on a hierarchical and distributed approach [13]. At the highest level there is a root node, which is responsible for the toplevel domain (IT in our case). The root node delegates the responsibility for the different second-level domains (e.g.: IT:UR, IT:FRD, etc.) to second-level naming authorities. Sub-domain responsibility can be further delegated using a virtually unlimited number of sub-levels (eg.: IT:UR:CNR, IT:UR:UNIMI, etc.). At the bottom of this hierarchy there are the leaf nodes, which are the only ones that harvest publication metadata from the actual repositories and assign unique identifiers to digital objects. Each agency adheres to the policy defined by the parent node and consistently defines the policies its child nodes must adhere to [13]. Within this architecture each node harvests PI information from its child nodes and is able to directly resolve all identifiers belonging to its domain and sub-domains. Besides, it can query other nodes to resolve NBN identifiers not belonging to its domain. A first testbed of the Italian infrastructure has been deployed and tests have been carried out for massive NBN assignment and for resolution of single identifiers.

156

4.6

M. Lancia et al.

PID Interoperability Issues

There are cases in which it may be useful to assign an NBN to a digital resource already having an assigned PI of a different type. For example, if a resource has an assigned DOI, it may still be useful to assign an NBN to it, because the latter type of PI has a syntax that facilitates accounting tasks (e.g. grouping and counting resources by sub-domain). In this cases it is useful to make provisions for easy retrieval of the previously assigned identifier. In our opinion there are two main ways for achieving this result: the first is to include the previously assigned PI in the final part of the NBN, the second is to include it in the metadata set. Both solutions have advantages and drawbacks. The first allow for easy retrieval of the alternate PI but may violate local policies for NBN generation and can be used for only one additional and pre-existing PI. The second makes it less immediate to retrieve the alternate PI but does not interfere in the NBN generation process. Also In this case, a common agreement is needed, which takes into account practical issues and existing situations [24]. 4.7

Duplicate Detection

The problem of duplicate detection within large and distributed document bases is not a trivial one but effective solutions in this field can have a major impact on the ability to certify contents, especially if combined with the use of persistent identifiers. A simple solution is to associate an MD5 (or equivalent) hash code to any resource. This code is calculated from the binary content of the file itself. Comparison of hash codes allow detecting bitwise equality but if two files differ for a single bit, they are considered different entities. More robust solutions can be devised using other techniques. We are currently exploring the possibility of using I-Match [25] or equivalent algorithms, which enable near duplicates detection. We are currently working to assess the scalability of this approach in large and distributed environments

5

Technical Solutions

A series of activities have been carried out and collaborations have been established in order to identify and implement appropriate technical solutions for the problems described in the previous sections. In particular, we have developed a first prototype of document repository called JDIAM which satisfy the following requirements: – – – – – –

Customizable workflows Customizable metadata sets Authority file management Document versioning Document classification Information retrieval

Cooperative Digital Asset Management in the Scientific Field

157

– User profiling – Support to a hierarchical organization of digital libraries – Support to OAI-PMH The application is based on open standards and open source applications. In particular, it has been developed on Java Enterprise platform and uses an Alfresco document repository. The interaction with the repository is performed via CMIS protocol. This means that Alfresco can be substituted by an CMIS compliant repository (e.g. Nuxeo). Future developments include: integration with the PI register for easy PI assignment, integration with automated classification tools for fast categorization of existing large document bases; enhancements of IR capabilities, DRM management, interoperability with external document bases through web service interface JDIAM will be interoperable with JNBN, a software for the management of the nodes of a distributed and hierarchical infrastructure for NBN assignment and resolution. JNBN has been developed by the National Research Council of Italy with contributions from Fondazione Rinascimento Digitale and is Powered by CNR’s JADA Development Framework. It is based on open technologies and standards and is released under EUPL license.

6

Conclusions

In this paper we have discussed the main issues that, in our opinion, should be addressed in order to improve the quality and reliability of the OA repositories and foster their use as dissemination tools, able to support research management and evaluation. OA repositories maybe leveraged both at the national and international level as relevant data sources for statistical surveys and bibliometric indicator construction [26]. In order to achieve this goal it is crucial to resolve all the problems of data certification, stability and reliability. In our opinion this goal can be achieved with a cooperative and flexible approach applied both on the policy and technical levels and involving all the relevant stakeholders of the scientific environment. Within CNR, experts in library and information science and in ICT are carrying out a series of joint studies and research activities aimed at designing technical and organizational solutions that can be exported to broader distributed and cooperative environments. To this end CNR is also involved in related activities with other relevant actors of the information and knowledge management community. The results of our work may represent a starting point for future cooperative developments and are made available to the scientific community in order to ensure the maximum consensus about the choices to be made.

158

M. Lancia et al.

References 1. MIUR: Ministero dellIstruzione, dellUniversit e della Ricerca: Decreto Ministeriale 28 luglio 2009, n. 89: valutazione dei titoli e delle pubblicazioni scientifiche (2009) 2. ERC: European Research Council: Relaunching the European Research Area, ERA (2007) 3. ERC: European Research Council: ERC Scientific Council Guidelines for Open Access (2007) 4. CRUI, Roma: CRUI. Gruppo Open Access: Linee guida per gli archivi istituzionali (2009) 5. Gu´edon, J.C.: Open Access and the divide between “mainstream” and “peripheral” science (2008) 6. Organization for Economic Co-operation and Development, Paris: Organization for Economic Co-operation and Development (OECD): Frascati Manual 2002: proposed standard practice for surveys on research and experimental development: the measurement of scientific and technological activities (2002) 7. Guerrini, M.: Gli archivi istituzionali: open access, valutazione della ricerca e diritto dautore. Bibliografica, 33–60 (2010) 8. Guy, M., Powell, A., Day, M.: Improving the quality of metadata in eprint archives. Ariadne 38 (2004) 9. Park, J.R.: Metadata quality in digital repositories: a survey of the current state of the art. Cataloging & Classification Quarterly 47, 213–228 (2009) 10. Lynch, C.: Institutional Repositories: essential infrastructure for scholarship in the digital age. ARL Bimonthly Report 226, 1–7 (2003) 11. White, W.: Institutional repositories: contributing to institutional knowledge management and the global research commons. In: Proceedings of the 4th International Open Repositories Conference. Georgia Institute of Technology, Atlanta (2009) 12. ISO: International Organization for Standardization – ISO TC 20/SC 13: Space data and information transfer systems: open archival information system: reference model: ISO 14721:2003 (2003) 13. Bellini, E., Cirinn´ a, C., Lancia, M., Lunghi, M., Puccinelli, R., Saccone, M., Sebastiani, B., Spasiano, M.: Persistent identifier distributed system for digital libraries. International Cataloguing and Bibliographic Control Journal (ICBC) 39, 30–36 (2010) 14. Paskin, N.: Digital Object Identifiers for scientific data. Data Science Journal 4, 1–20 (2005) 15. ARK: Archival Resource Key (2011) 16. Kunze, J., Rodgers, R.: The ARK identifier scheme. Technical report, Network Preservation Group (2008) 17. Sun, S., Lannom, L., Boesch, B.: Handle system overview: RFC 3650. Technical report, The Internet Society (ISOC) – IETF (2003) 18. Sun, S., Reilly, S., Lannom, L.: Handle system namespace and service definition: RFC 3651. Technical report, The Internet Society (ISOC) – IETF (2003) 19. Sun, S., Reilly, S., Lannom, L., Petrone, J.: Handle system protocol (ver 2.1) specification: RFC 3652. Technical report, The Internet Society (ISOC) – IETF (2003) 20. Library of Congress – National Digital Library Program: Library of Congress: Relationship Between URNs, Handles, and PURLs (1997) 21. Library of Congress: Library of Congress – Network Development MARC Standards Office: Library of Congress Control Number (LCCN): restructuring to accommodate century change (1999)

Cooperative Digital Asset Management in the Scientific Field

159

22. Hakala, J., Hones, A.: Using national bibliography numbers as uniform resource names: draft IETF urnbis RFC 3188bis-nbn-urn-00. Technical report, IETF (2010) 23. Hakala, J.: Using national bibliography numbers as uniform resource names: RFC 3188. Technical report, IETF (2001) 24. Lancia, M., Sebastiani, B., Puccinelli, R., Spasiano, M., Saccone, M., Trufelli, L., Bellini, E., Cirinn´ a, C., Lunghi, M.: Towards a european global resolver service of persistent identifiers. In: Cirinn´ a, C., Lunghi, M. (eds.) Cultural Heritage Online Empowering Users: an Active Role for Users Communities, Florence, pp. 137–142 (2009) 25. Chowdury, A., Frieder, O., Grossmann, D., McCabe, M.C.: Collection statistics for fast duplicate document detection. ACM Transactions on Information Systems (TOIS) 20, 171–191 (2002) 26. Harnad, S.: Open access scientometrics and the UK research assessment exercise. Scientometrics 79, 147–156 (2009)

Author Index

Agosti, Maristella 61, 87, 100, 111 Alber, Birgit 61, 100 Aloia, Nicola 104, 127 Appice, Annalisa 132

Meghini, Carlo 104, 127 Miotto, Riccardo 26, 100

Basile, Teresa M.A. 38, 119, 123 Beninc` a, Paola 100 Biba, Marenglen 119

Orio, Nicola

Ceci, Michelangelo 115, 132 Concordia, Cesare 104, 127 Crivellari, Franco 130 Deambrosis, Graziano 130 Di Buccio, Emanuele 140 Di Mauro, Nicola 1, 107, 119 Di Nunzio, Giorgio Maria 61, 100, 130 Dussin, Marco 61, 100, 130, 140 Esposito, Floriana 123, 132

1, 38, 107, 119,

Ferilli, Stefano 38, 107, 115, 119, 123 Ferrara, Felice 14, 136 Ferro, Nicola 87, 130, 140 Lancia, Maurizio Loglisci, Corrado

144 115, 132

Malerba, Donato 115, 132 Manfioletti, Marta 49 Masiero, Ivano 140

Nicchio, Mattia

49

26, 111

Pescarini, Diego 61, 100 Puccinelli, Roberto 144 Pudota, Nirmala 14 Rabanus, Stefan 61, 100 Redavid, Domenico 123 Rigon, Andreina 87 Saccone, Massimiliano 144 Silvello, Gianmaria 87, 140 Spasiano, Marco 144 Spyratos, Nicolas 75 Sugibuchi, Tsuyoshi 75 Taranto, Claudio 1, 107 Tasso, Carlo 14, 136 Terenzoni, Erilde 87 Tomaselli, Alessandra 61, 100 Tommasi, Cristina 87 Trufelli, Luciana 144 Villani, Incoronata Yang, Jitao

75

123