Research and Advanced Technology for Digital Libraries, 11 conf., ECDL 2007

Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris ...

Author: Laszlo Kovacs

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Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany

4675

László Kovács Norbert Fuhr Carlo Meghini (Eds.)

Research and Advanced Technology for Digital Libraries 11th European Conference, ECDL 2007 Budapest, Hungary, September 16-21, 2007 Proceedings

13

Volume Editors László Kovács Hungarian Academy of Sciences Computer and Automation Research Institute Department of Distributed Systems H-1111 Budapest, XI. Lágymányosi u. 11., Hungary E-mail: [email protected] Norbert Fuhr University of Duisburg-Essen Information Systems Department of Computational and Cognitive Sciences 47048 Duisburg, Germany E-mail: [email protected] Carlo Meghini Consiglio Nazionale delle Ricerche Istituto di Scienza e Tecnologie della Informazione 56124 Pisa, Italy E-mail: [email protected]

Library of Congress Control Number: 2007934833 CR Subject Classification (1998): H.3.7, H.2, H.3, H.4.3, H.5, J.7, J.1, I.7 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. Internet/Web and HCI ISSN ISBN-10 ISBN-13

0302-9743 3-540-74850-4 Springer Berlin Heidelberg New York 978-3-540-74850-2 Springer Berlin Heidelberg New York

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. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12121076 06/3180 543210

Preface

We are proud to present the proceedings of the 11th European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2007) which, following Pisa (1997), Heraklion (1998), Paris (1999), Lisbon (2000), Darmstadt (2001), Rome (2002), Trondheim (2003), Bath (2004), Vienna (2005) and Alicante (2006), took place on September 16-21, 2007 in Budapest, Hungary. Over the last 11 years, ECDL has created a strong interdisciplinary community of researchers and practitioners in the field of digital libraries, and has formed a substantial body of scholarly publications contained in the conference proceedings. ECDL 2007 featured separate calls for paper and poster submissions, resulting in 119 full papers and 34 posters being submitted to the conference. All papers were subject to an in-depth peer-review process; three reviews per submission were produced by a Program Committee of 69 members from 27 countries. In total 36 of 119 full paper submissions were accepted at the Program Committee meeting for presentation at the conference and publication in the proceedings with Springer, resulting in an acceptance rate of 30%. Also, 24 poster/demo submissions and another 15 papers from the full paper submissions were accepted for poster presentation and publication in the proceedings volume. ECDL 2007 was devoted to discussions about hot issues and applications and primarily provided a forum to reinforce the collaboration of researchers and practicioners.The main conference consisted of 12 technical sessions and a poster /demo session on the following topics: Ontologies, Digital Libraries and the Web, Models, Multimedia and Multilingual Digital Libraries, Grid and Peerto-Peer, Preservation, User Interfaces, Document Linking, Information Retrieval, Personal Information Management, New DL Applications and User Studies. The conference featured two panels, which addressed timely and important topics, namely, experiences of DL projects in synergy with the European Commission’s initiatives in the panel “On the Move Towards the European Digital Library: BRICKS, TEL, MICHAEL and DELOS converging experiences” chaired by Massimo Bertoncini and the special challenges of Digital Library research and development in the host region of the conference in the panel “Digital Libraries in Central and Eastern Europe: Infrastructure Challenges for the New Europe” chaired by Christine Borgman. The keynote talk by Seamus Ross (Humanities Computing and Information Management, University of Glasgow) addressed the questions of digital preservation, while the keynote talk by Arne Solvberg (Dept. of Computer and Information Science, Norwegian University of Science and Technology) focused on the challenges of WiFi-Trondheim – an experiment in providing Broadband Everywhere for All.

VI

Preface

The preceding tutorials provided further in-depth looks at areas of current interest, including “Thesauri and Ontologies in DLs by Dagobert Soegrel, Introduction to DLs” by Ed Fox, “Approaches for Large Scale Digital Library Infrastructures” by Thomas Risse, and “Building DLs On-Demand by Sharing Content, Services and Computing Resources” by Donatella Castelli. The workshops, held in conjunction with ECDL2007, covered wide areas of interest: CLEF 2007 – Cross-Language Evaluation Forum, Workshop on “Foundations of Digital Libraries”; LADL 2007 – Cross-Media and Personalized Learning Applications on Top of Digital Libraries, Curriculum Development in Digital Libraries: An Exercise in Developing Lesson Plans , Towards a European Repository Ecology: Conceptualizing Interactions Between Networks of Repositories and Services, NKOS -Networked Knowledge Organization Systems and Services, and Libraries in the Digital Age: What If. . . ? We would like to take the opportunity to thank everybody who made this conference possible, all the conference participants and presenters, who provided an exciting full-week program of high technical quality. We greatly appreciate the contribution of the Program Committee members, who did an outstanding reviewing job under tight time constraints; and we are grateful to all Chairs and members of the Organization Committee, who worked hard to make the best out of the Conference. Finally, we would also like to thank the conference sponsors and cooperating agencies: the Computer and Automation Research Institute of the Hungarian Academy of Sciences (MTA SZTAKI), the DELOS Network of Excellence on Digital Libraries, and the Hungarian Tourism Office. September 2007

László Kov´ acs Norbert Fuhr Carlo Meghini

Organization

Organization Committee General Chair László Kov´ acs

Program Co-chairs Norbert Fuhr

Carlo Meghini

Department of Distributed Systems, Computer and Automation Research Institute, Hungarian Academy of Sciences, Hungary

Information Systems Faculty of Engineering Sciences, University of Duisburg-Essen, Germany Consiglio Nazionale delle Ricerche Istituto di Scienza e Tecnologie dell’Informazione, Italy

Workshops Chairs Maristella Agosti Birte Christensen-Dalsgaard

University of Padua, Italy State and University Library, Denmark

Poster and Demo Chairs Ulrike Steffens José Borbinha

OFFIS, Germany DEI/IST/UTL and INESC-ID, Portugal

Tutorials Chair Rudi Schmiede

Darmstadt University of Technology, Germany

Publicity and Exhibit Chairs Yuzuru Tanaka for Asia Meme Media Laboratory, Hokkaido University, Japan Jane Hunter for Australia School of ITEE, Australia Hussein Suleman for Africa University of Cape Town, South Africa Panel Chairs Seamus Ross Edward Fox

University of Glasgow, UK Virginia Tech / Dept. of Computer Science, USA

Doctoral Consortium Chairs Tiziana Catarci University of Rome 1, Italy Nicolas Spyratos Université de Paris-Sud, France

VIII

Organization

Local Arrangements Chair Gusztáv Hencsey Computer and Automation Research Institute, Hungarian Academy of Sciences, Hungary

Program Committee Hanne Albrechtsen Margherita Antona Tom Baker Nicholas Belkin Maria Bielikov´ a George Buchanan Gerhard Budin Tiziana Catarci José H. Can´ os Cerda Hsinchun Chen Anita S.Coleman Gregory Crane Sally Jo Cunningham M´ ario J. Gaspar da Silva Pablo de la Fuente Susanne Dobratz Boris V.Dobrov Jacques Ducloy Lim Ee-Peng Floriana Esposito Schubert Foo Edward Fox Richard Furuta Stefan Gradmann Allan Hanbury Donna Harman Djoerd Hiemstra Jen-Shin Hong Leonid Kalinichenko Sarantos Kapidakis Claus-Peter Klas Traugott Koch Harald Krottmaier Carl Lagoze Mounia Lalmas Ronald Larsen

Institute of Knowledge Sharing, Denmark FORTH, Greece State and University Library, Germany Rutgers University, USA Slovak University of Technology in Bratislava, Slovakia University of Wales, Swansea University of Vienna, Austria University of Rome 1, Italy Universidad Politecnica de Valencia, Spain University of Arizona, Tucson, USA University of Arizona, USA Tufts University, USA University of Waikato, New Zealand Universidade de Lisboa, Portugal University of Valladolid, Spain Humboldt University, Germany Moscow State University, Russia CNRS-INIST, France Nanyang Technological University, Singapore University of Bari, Italy Nanyang Technological University, Singapore Virginia Tech, USA Texas A & M University, USA University of Hamburg, Computing Center, Germany Vienna University of Technology, Austria NIST, USA Twente University, The Netherlands Department of Computer Science, National ChiNan University, Taiwan Russian Academy of Sciences, Russia Ionian University, Greece University of Duisburg, Germany Max Planck Digital Library, Germany Graz University of Technology, Austria Cornell University, USA Queen Mary University of London, UK University of Pittsburgh, USA

Organization

Ray Larson Clifford Lynch Antonio Polo M´ arquez Catherine C. Marshall Andr´ as Micsik Reagan Moore Marc Nanard Liddy Nevile Fernando L´ opez Ostenero Anselmo Pena˜s Dimitris Plexousakis Andy Powell Hansen Preben Andreas Rauber Thomas Risse Laurent Romary Lloyd Rutledge J. Alfredo S´ anchez Heiko Schuldt Timos Sellis Dagobert Soergel Ingeborg Solvberg Jela Steinerova Shigeo Sugimoto Tamara Sumner Jes´ us Tramullas Omar Valdiviezo Herbert Van de Sompel Ian Witten

IX

University of California, Berkeley, USA Coalition for Networked Information, USA University of Extremadura, Spain Microsoft Corporation, Redmond,WA, USA MTA SZTAKI, Hungary SDSC, USA University of Montpellier, France La Trobe University, Australia UNED, Spain UNED, Spain FORTH, Greece Eduserv Foundation, UK SICS, Sweden University of Technology, Vienna, Austria Fraunhofer IPSI, Germany Laboratoire Loria CNRS, France CWI, The Netherlands Universidad de las Americas Puebla, Mexico University of Basel, Switzerland National Technical University of Athens, Greece University of Maryland, USA Norwegian University of Technology and Science, Norway Comenius University in Bratislava, Slovakia Graduate School of Library, Information and Media Studies, University of Tsukuba, Japan University of Colorado, Boulder, USA University of Zaragoza, Spain Universidad de las Americas Puebla, Mexico Los Alamos National Laboratory, USA University of Waikato, New Zealand

Table of Contents

Ontologies The Semantic GrowBag Algorithm: Automatically Deriving Categorization Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J¨ org Diederich and Wolf-Tilo Balke

1

Ontology-Based Question Answering for Digital Libraries . . . . . . . . . . . . . Stephan Bloehdorn, Philipp Cimiano, Alistair Duke, Peter Haase, J¨ org Heizmann, Ian Thurlow, and Johanna V¨ olker

14

Formalizing the Get-Specific Document Classification Algorithm . . . . . . . Fausto Giunchiglia, Ilya Zaihrayeu, and Uladzimir Kharkevich

26

Digital Libraries and the Web Trustworthiness Analysis of Web Search Results . . . . . . . . . . . . . . . . . . . . . Satoshi Nakamura, Shinji Konishi, Adam Jatowt, Hiroaki Ohshima, Hiroyuki Kondo, Taro Tezuka, Satoshi Oyama, and Katsumi Tanaka Improved Publication Scores for Online Digital Libraries Via Research Pyramids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sulieman Bani-Ahmad and Gultekin Ozsoyoglu Key Element-Context Model: An Approach to Efficient Web Metadata Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ba-Quy Vuong, Ee-Peng Lim, Aixin Sun, Chew-Hung Chang, Kalyani Chatterjea, Dion Hoe-Lian Goh, Yin-Leng Theng, and Jun Zhang

38

50

63

Models A Cooperative-Relational Approach to Digital Libraries . . . . . . . . . . . . . . . Alessio Malizia, Paolo Bottoni, Stefano Levialdi, and Francisco Astorga-Paliza

75

Mind the (Intelligibility) Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yannis Tzitzikas and Giorgos Flouris

87

Using XML Logical Structure to Retrieve (Multimedia) Objects . . . . . . . Zhigang Kong and Mounia Lalmas

100

XII

Table of Contents

Multimedia and Multilingual DLs Lyrics-Based Audio Retrieval and Multimodal Navigation in Music Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meinard M¨ uller, Frank Kurth, David Damm, Christian Fremerey, and Michael Clausen Automatic Identification of Music Works Through Audio Matching . . . . . Riccardo Miotto and Nicola Orio Roadmap for MultiLingual Information Access in the European Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maristella Agosti, Martin Braschler, Nicola Ferro, Carol Peters, and Sjoerd Siebinga

112

124

136

Grid and Peer-to-Peer MinervaDL: An Architecture for Information Retrieval and Filtering in Distributed Digital Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christian Zimmer, Christos Tryfonopoulos, and Gerhard Weikum A Grid-Based Infrastructure for Distributed Retrieval . . . . . . . . . . . . . . . . Fabio Simeoni, Leonardo Candela, George Kakaletris, Mads Sibeko, Pasquale Pagano, Giorgos Papanikos, Paul Polydoras, Yannis Ioannidis, Dagfinn Aarvaag, and Fabio Crestani VIRGIL – Providing Institutional Access to a Repository of Access Grid Sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ron Chernich, Jane Hunter, and Alex Davies

148 161

174

Preservation Opening Schr¨ odingers Library: Semi-automatic QA Reduces Uncertainty in Object Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lars R. Clausen

186

Texts, Illustrations, and Physical Objects: The Case of Ancient Shipbuilding Treatises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carlos Monroy, Richard Furuta, and Filipe Castro

198

Trustworthy Digital Long-Term Repositories: The Nestor Approach in the Context of International Developments . . . . . . . . . . . . . . . . . . . . . . . . . . Susanne Dobratz and Astrid Schoger

210

User Interfaces Providing Context-Sensitive Access to the Earth Observation Product Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stephan Kiemle and Burkhard Freitag

223

Table of Contents

XIII

T-Scroll: Visualizing Trends in a Time-Series of Documents for Interactive User Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yoshiharu Ishikawa and Mikine Hasegawa

235

Thesaurus-Based Feedback to Support Mixed Search and Browsing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edgar Meij and Maarten de Rijke

247

Document Linking Named Entity Identification and Cyberinfrastructure . . . . . . . . . . . . . . . . . Alison Babeu, David Bamman, Gregory Crane, Robert Kummer, and Gabriel Weaver

259

Finding Related Papers in Literature Digital Libraries . . . . . . . . . . . . . . . . Nattakarn Ratprasartporn and Gultekin Ozsoyoglu

271

Extending Semantic Matching Towards Digital Library Contexts . . . . . . . L´ aszl´ o Kov´ acs and Andr´ as Micsik

285

Information Retrieval Towards a Unified Approach Based on Affinity Graph to Various Multi-document Summarizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaojun Wan and Jianguo Xiao

297

Large-Scale Clustering and Complete Facet and Tag Calculation . . . . . . . Bolette Ammitzbøll Madsen

309

Annotation-Based Document Retrieval with Probabilistic Logics . . . . . . . Ingo Frommholz

321

Personal Information Management Evaluation of Visual Aid Suite for Desktop Searching . . . . . . . . . . . . . . . . . Schubert Foo and Douglas Hendry

333

Personal Environment Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna Zacchi and Frank Shipman

345

Empirical Evaluation of Semi-automated XML Annotation of Text Documents with the GoldenGATE Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . Guido Sautter, Klemens B¨ ohm, Frank Padberg, and Walter Tichy

357

New DL Applications Exploring Digital Libraries with Document Image Retrieval . . . . . . . . . . . Simone Marinai, Emanuele Marino, and Giovanni Soda

368

XIV

Table of Contents

Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jillian C. Wallis, Christine L. Borgman, Matthew S. Mayernik, Alberto Pepe, Nithya Ramanathan, and Mark Hansen Digital Libraries Without Databases: The Bleek and Lloyd Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hussein Suleman

380

392

User Studies A Study of Citations in Users’ Online Personal Collections . . . . . . . . . . . . Nishikant Kapoor, John T. Butler, Sean M. McNee, Gary C. Fouty, James A. Stemper, and Joseph A. Konstan

404

Investigating Document Triage on Paper and Electronic Media . . . . . . . . George Buchanan and Fernando Loizides

416

Motivating and Supporting User Interaction with Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andreas W. Neumann

428

Panels On the Move Towards the European Digital Library: BRICKS, TEL, MICHAEL and DELOS Converging Experiences . . . . . . . . . . . . . . . . . . . . . Massimo Bertoncini Digital Libraries in Central and Eastern Europe: Infrastructure Challenges for the New Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christine L. Borgman, Tatjana Aparac-Jeluˇsić, Sonja Pigac Ljubi, Zinaida Manˇzuch, Gy¨ orgy Sebestyén, and Andr´ as G´ abor

440

442

Posters and Demos Electronic Work : Building Dynamic Services over Logical Structure Using Aqueducts for XML Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miguel A. Mart´ınez-Prieto, Pablo de la Fuente, Jes´ us Vegas, and Joaqu´ın Adiego

445

A Model of Uncertainty for Near-Duplicates in Document Reference Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Claudia Hess and Michel de Rougemont

449

Assessing Quality Dynamics in Unsupervised Metadata Extraction for Digital Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Ivanyukovich, Maurizio Marchese, and Patrick Reuther

454

Table of Contents

Bibliographical Meta Search Engine for the Retrieval of Scientific Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Artur Gajek, Stefan Klink, Patrick Reuther, Bernd Walter, and Alexander Weber

XV

458

In-Browser Digital Library Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hussein Suleman

462

Evaluating Digital Libraries with 5SQual . . . . . . . . . . . . . . . . . . . . . . . . . . . . B´ arbara L. Moreira, Marcos A. Gon¸calves, Alberto H.F. Laender, and Edward A. Fox

466

Reducing Costs for Digitising Early Music with Dynamic Adaptation . . . Laurent Pugin, John Ashley Burgoyne, and Ichiro Fujinaga

471

Supporting Information Management in Digital Libraries with Map-Based Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rudolf Mayer, Angela Roiger, and Andreas Rauber

475

Policy Decision Tree for Academic Digital Collections . . . . . . . . . . . . . . . . . Alexandros Koulouris and Sarantos Kapidakis

481

Personalized Faceted Browsing for Digital Libraries . . . . . . . . . . . . . . . . . . Michal Tvaroˇzek and M´ aria Bielikov´ a

485

The Use of Metadata in Visual Interfaces to Digital Libraries . . . . . . . . . . Ali Shiri

489

Location and Format Independent Distributed Annotations for Collaborative Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fabio Corubolo, Paul B. Watry, and John Harrison

495

NSDL MatDL: Adding Context to Bridge Materials e-Research and e-Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laura Bartolo, Cathy Lowe, Dean Krafft, and Robert Tandy

499

A Framework for the Generation of Transformation Templates . . . . . . . . . Manuel Llavador and José H. Can´ os

501

MultiMatch – Multilingual/Multimedia Access to Cultural Heritage . . . . Giuseppe Amato, Juan Cigarr´ an, Julio Gonzalo, Carol Peters, and Pasquale Savino

505

The Future of Large-Scale Evaluation Campaigns for Information Retrieval in Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maristella Agosti, Giorgio Maria Di Nunzio, Nicola Ferro, Donna Harman, and Carol Peters Digital 101: Public Exhibition System of the National Digital Archives Program, Taiwan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ku-Lun Huang and Hsiang-An Wang

509

513

XVI

Table of Contents

aScience: A Thematic Network on Speech and Tactile Accessibility to Scientific Digital Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristian Bernareggi and Gian Carlo Dalto

515

PROBADO – A Generic Repository Integration Framework . . . . . . . . . . . Harald Krottmaier, Frank Kurth, Thorsten Steenweg, Hans-J¨ urgen Appelrath, and Dieter Fellner

518

VCenter: A Digital Video Broadcast System of NDAP Taiwan . . . . . . . . . Hsiang-An Wang, Chih-Yi Chiu, and Yu-Zheng Wang

522

Retrieving Tsunami Digital Library by Use of Mobile Phones . . . . . . . . . . Sayaka Imai, Yoshinari Kanamori, and Nobuo Shuto

525

Using Watermarks and Offline DRM to Protect Digital Images in DIAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hsin-Yu Chen, Hsiang-An Wang, and Chin-Lung Lin

529

CIDOC CRM in Action – Experiences and Challenges . . . . . . . . . . . . . . . . Philipp Nussbaumer and Bernhard Haslhofer

532

The Legal Environment of Digital Curation – A Question of Balance for the Digital Librarian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mags McGinley

534

Demonstration: Bringing Lives to Light: Browsing and Searching Biographical Information with a Metadata Infrastructure . . . . . . . . . . . . . Ray R. Larson

539

Repository Junction and Beyond at the EDINA (UK) National Data Centre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robin Rice, Peter Burnhill, Christine Rees, and Anne Robertson

543

A Scalable Data Management Tool to Support Epidemiological Modeling of Large Urban Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christopher L. Barrett, Keith Bisset, Stephen Eubank, Edward A. Fox, Yi Ma, Madhav Marathe, and Xiaoyu Zhang

546

Living Memory Annotation Tool – Image Annotations for Digital Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wolfgang Jochum, Max Kaiser, Karin Schellner, and Franz Wirl

549

A User-Centred Approach to Metadata Design . . . . . . . . . . . . . . . . . . . . . . . Emma Tonkin

551

A Historic Documentation Repository for Specialized and Public Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristina Ribeiro, Gabriel David, and Catalin Calistru

555

Table of Contents

XVII

Finding It on Google, Finding It on del.icio.us. . . . . . . . . . . . . . . . . . . . . . . Jacek Gwizdka and Michael Cole

559

DIGMAP – Discovering Our Past World with Digitised Maps . . . . . . . . . . José Borbinha, Gilberto Pedrosa, Diogo Reis, Jo˜ ao Luzio, Bruno Martins, Jo˜ ao Gil, and Nuno Freire

563

Specification and Generation of Digital Libraries into DSpace Using the 5S Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Douglas Gorton, Weiguo Fan, and Edward A. Fox

567

EOD - European Network of Libraries for eBooks on Demand . . . . . . . . . Zolt´ an Mez˝ o, Sonja Svoljˇsak, and Silvia Gstrein

570

Semantics and Pragmatics of Preference Queries in Digital Libraries . . . . El hadji Mamadou Nguer

573

Applications for Digital Libraries in Language Learning and the Professional Development of Teachers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alannah Fitzgerald

579

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Semantic GrowBag Algorithm: Automatically Deriving Categorization Systems Jörg Diederich and Wolf-Tilo Balke L3S Research Center and Leibniz Universität Hannover, Hanover, Germany {diederich,balke}@l3s.de

Abstract. Using keyword search to find relevant objects in digital libraries often results in way too large result sets. Based on the metadata associated with such objects, the faceted search paradigm allows users to structure and filter the result set, for example, using a publication type facet to show only books or videos. These facets usually focus on clear-cut characteristics of digital items, however it is very difficult to also organize the actual semantic content information into such a facet. The Semantic GrowBag approach, presented in this paper, uses the keywords provided by many authors of digital objects to automatically create light-weight topic categorization systems as a basis for a meaningful and dynamically adaptable topic facet. Using such emergent semantics enables an alternative way to filter large result sets according to the objects’ content without the need to manually classify all objects with respect to a pre-specified vocabulary. We present the details of our algorithm using the DBLP collection of computer science documents and show some experimental evidence about the quality of the achieved results. Keywords: faceted search, category generation, higher-order co-occurrence.

1 Introduction Due to today’s sophisticated ranking techniques, the simple keyword search paradigm has been remarkably successful in finding relevant resources in huge data collections, such as digital libraries or even the world wide web. One remaining problem, however, is that users are often unsure which actual keywords to choose so that finding a particular resource often involves several search queries, which then have to be manually refined step-by-step according to the result set of the previous keyword search. The faceted search [1,2,3,4] paradigm makes this process of refining queries explicit and presents the results along with several orthogonal facets, which characterize the result set (e.g., a ‘publication type’ facet might reveal that there are only two videos among possibly 10,000 relevant results) and thus allow the user to restrict the result set in an easy and intuitive way by exploiting metadata. This paper is focused on facets based on the actual content of the objects, which allow to restrict the result set to a specific topic. To limit the size of such a topic facet, a hierarchical system is typically used to structure the facet, for example, using the Dewey Decimal Classification System, the ACM curriculum or any other cataloguing system (cf. Fig. 1). Such a topic facet can be used in several different ways: L. Kovács, N. Fuhr, and C. Meghini (Eds.): ECDL 2007, LNCS 4675, pp. 1–13, 2007. c Springer-Verlag Berlin Heidelberg 2007

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J. Diederich and W.-T. Balke Semantic Web Ontologies

RDF OWL

Rules

Ontology reasoners

Fig. 1. Example categorization system for topics

– To filter results according to a whole subtree (e.g., if a user selects ‘ontologies’ in a facet based on the categories in Fig. 1, all objects about ‘OWL’ and ‘Ontology reasoners’ can be automatically included in the result set, too). – To find communities (e.g., for a query ‘XML’, the facet might show a large number of objects in ‘RDF’ and in ‘OWL’, but not in ‘Rules’). – To characterize authors or groups of authors (e.g., showing that the objects authored by a particular person are mainly about the topics ‘OWL’ and ‘Rules’). – To characterize the object collection as a whole (e.g., if a query ‘rules’ only provides results related to ‘Semantic Web’, but not about ‘business rules’). The main problem, however, is creating and maintaining the underlying topic categorization system, which is often done manually and especially difficult for very dynamic domains such as computer science research. We have already presented first demonstrations of the basic applicability of our approach [5,6] for topic categorization, but not yet disclosed and evaluated our algorithm’s specifics. This paper details our Semantic GrowBag approach to automatically organize topics in light-weight hierarchical categorization systems (so-called GrowBag graphs), starting from the author keywords available in large digital object collections such as DBLP for the computer science domain, or Medline for the medical domain. In Sect. 2 we briefly investigate related work for faceted searches and taxonomy generation. Section 3 presents our Semantic GrowBag algorithm and its basic characteristics. Using the DBLP data set we explain our algorithm in detail in Sect. 4 along practical use cases and evaluate the derived GrowBag graphs. In this way, we give a good intuition about the algorithm’s practical impact and show that the (rather limited) tagging with keywords in today’s collections is already useful to derive good and intuitive topic facets. The paper closes with a short summary and outlook.

2 Related Work The basic idea of faceted search was built on the scatter/gather clustering approaches [7] for document browsing. The hierarchical organization of result documents according to certain faceted categories has been shown to enable intuitive user interfaces superior to the presentation of ranked lists [8]. But for the actual creation of good faceted categories some degree of manual interaction is still needed and all categories of interest must always be known in advance, thus, important emerging trends in the data may not be shown in faceted interfaces [1,2,3]. We will see in the practical use cases that the Semantic GrowBag algorithm overcomes some of these problems by automatically

The Semantic GrowBag Algorithm: Automatically Deriving Categorization Systems

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deriving faceted categories on the fly and can even reflect trends in the taggings by selecting only parts of the underlying document base. Another related technology are so-called topic maps [9] that were defined as an XML-based data format (standardized in 1999 as ISO/IEC 13250) to formulate simple structures for knowledge representation. The topic maps defined ‘associations’ between topics and ‘occurrences’ that linked topics to documents, e.g., on the WWW. In contrast to our approach, topic maps therefore base on the idea of reflecting more or less static thesauri and indexes like e.g., the topic hierarchy of the Open Directory Project (ODP). For the (to some degree) automatic creation of ontologies there are several approaches mostly relying on (supervised) learning techniques based on natural language processing, e.g., using language models or syntactic contexts [10,11]. These approaches identify synonyms, sub-/superclass hierarchies, etc. from full texts by relying on the sentence structure, where phrases like ‘such as. . . ’ imply a certain hierarchy between terms. Moreover, the belief in the correctness of derived classes and/or hierarchies can be supported by comparison to general ontologies like WordNet or counting co-occurrences, e.g., in documents retrieved by Google. In contrast to our approach these techniques aim to understand the whole information space concerned with a topic and not the most discriminating facets as given by a collection. Sanderson and Croft [12] have proposed a basic approach to automatically create categorization systems by exploiting keyword co-occurrences. Keyword Y is defined as subsuming keyword X if at least 80% of the objects tagged with X are also tagged with Y , and Y occurs more frequently than X. Although manually determined subsumption relations usually have a much stronger semantics, already this simple subsumption definition has shown a nice performance for navigational purposes [12] when using keywords extracted from full text. However, for manually specified keywords, authors tend to use only ‘leaf’ categories they have in mind, so two subsuming keywords hardly co-occur in more than 80% of the cases.

3 The Semantic GrowBag Algorithm The basic idea of the Semantic GrowBag algorithm is to automatically create categorization systems from a corpus of digital objects (like documents, images, etc.) annotated with keywords. This is done by exploiting higher-order co-occurrence, known from computational linguistics [13]. As shown in Fig. 2, keywords X and Z are associated with one object (C), which is a first order co-occurrence or simply co-occurrence. Keywords Y and Z are not associated with the same object, but there may still be a subsuming keyword (X) which is associated with objects (B and C) that are tagged by both keywords Y and Z, respectively. Such second order co-occurrences (or generally higher-order co-occurrences) occur more often than first-order occurrences alone

Keyword Y Object A

Keyword X Object B

Keyword Z Object C

metadata level instance level

Fig. 2. Example {first|second}-order co-occurrence

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J. Diederich and W.-T. Balke

and, hence, reduce the sparsity of the co-occurrence dataset. They have also been found to be more robust than first-order co-occurrences, for example, improving word sense disambiguation algorithms [13]. Including higher-order co-occurrences has two main effects: (1) Finding additional (‘hidden’) relations between keywords which cannot be found using first-order cooccurrences only. (2) Changing the ‘strength’ (i.e., the number) of existing first-order co-occurrences to include higher-order co-occurrences. The Semantic GrowBag algorithm uses a biased PageRank algorithm [14] for the computation of the higher-order co-occurrences, as the properties of PageRank are well understood, it can be computed very efficiently and converges to a stable solution for appropriate input data (e.g., in the SimRank approach [15], PageRank has been used in a similar way to compute transitive similarities). Our algorithm comprises the following three steps, which are explained in more detail in the upcoming sections. I. Compute a new co-occurrence metric including higher-order co-occurrence. II. Find relations between keywords, based on the new co-occurrence metric. III. Construct for each keyword i a single GrowBag graph to present a limited view on the ‘neighborhood’ of keyword i instead of having one manually crafted graph as for legacy classifications/thesauri which covers the whole object collection. 3.1 Part I: Higher-Order Co-occurrences In a nutshell, computing the co-occurrence metric including higher-order co-occurrences comprises the following three basic steps: 1. Create an (nxn) matrix M based on weighted (first-order) co-occurrence relations for the n keywords. 2. For each keyword i, determine the most often co-occurring keywords (the direct neighbors). 3. For each keyword i, compute the Biased PageRank scores using matrix M and biasing on the direct neighbors of keyword i. The resulting n PageRank score vectors are complementary to the n lists of most often co-occurring keywords, but additionally include higher-order co-occurrences due to the flow of the scores in the PageRank graph. Specifically, the ranking imposed by the scores in the PageRank score vector of keyword i now determines the hidden related keywords to keyword i and is, hence, an enhancement of the legacy ‘related keywords’ notion.1 The following sections provide more details. Co-occurrence Matrix M . The elements m(j, i) of matrix M are defined as follows: cooc(i, j) ∗ ICF (i) m(j, i) = j cooc(i, j) ∗ ICF (i) 1

(1)

Latent semantic analysis [16,17] is very related here, which basically performs a singular value decomposition on the co-occurrence matrix to find ‘latent’ topics, i.e., abstract topics given by linear combinations of keywords. However, it cannot find subsumption hierarchies.


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with cooc(i, j) being the (first order) co-occurrences of keywords i and j, i.e., the number of objects that are tagged with both keywords2 . The inverse co-occurrence frequency (ICF) much resembles the inverse document frequency as known from information retrieval [18] and is defined as follows: ICF (i) = log(

Overall # of keywords ) total # of keywords co-occurring with keyword i

(2)

Multiplying the ICF of i to cooc(i, j) in eq. (1) has the effect that m(j, i) is decreased for those keywords i that co-occur with many other keywords. These have, thus, a less discriminating power. The above defined matrix M has the following properties: It is symmetric in terms of links, i.e., if m(j, i) = 0 then m(i, j) = 0. Hence, the PageRank graph defined by M does not contain dangling nodes or rank sinks. Though symmetric in terms of links, it is not symmetric in terms of weights: The normalization in the denominator of eq. (1) ensures that m(j, i) = m(i, j) since the sum of all co-occurrences is typically not the same for all pairs of keywords. Therefore, the PageRank graph defined by M is a directed graph so that the PageRank scores do not converge to the number of co-occurring keywords for each keyword. Hence, matrix M is stochastic, irreducible, and primitive because of normalization and because we apply the Random Jump vector as defined by Page and Brin [14] (with the usual 15% random jump probability). Therefore, the PageRank computation converges to the principal Eigenvector [19]. Direct Neighbors: Top-X. To find the direct neighbors for a given keyword ti , we first sort the vector with all co-occurring keywords for ti . We then define the direct neighbors as the top-X elements of the sorted vector which accumulate the first P % of the sum of all vector values (i.e., the integral of the co-occurrence graph). P is the essential parameter of our algorithm that controls to which degree higher-order co-occurrence should be included. A typical value found useful in practice is P ∈ [10 : 30]%. Biasing on Top-X. For computing the biased PageRank scores, we initialize the start vector and the random jump vector with 1 to provide each node in the PageRank graph with some default score. This step is the most costly part of our approach in terms of time complexity, but as the overall set of author keywords will remain stable (they are never changed and changes are relatively small for large object collections), our algorithm can be computed off-line and re-run periodically to update the results according to the added author keywords. 3.2 Part II: Finding Hidden Relations To find relations between two keywords i and j, the PageRank score vectors for the keywords i and j are used as follows: 2

Using Dice or Jaccard similarity instead of co-occurrences has been found inferior as they give a rather high weight for rarely occurring terms, which does not support the notion of ‘emergent’ semantics.

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1. Sort the power-law distributed PageRank score vectors and cut the tail to filter out all keywords with too low scores. 2. Compare the scores of keywords i and j in those two sorted and filtered score vectors. If both i and j exist in both vectors and either i or j has a higher score in both vectors, the one with the higher score is a candidate for subsuming the one with the lower ranks. 3. Post-filter candidates based on their rank in both score vectors and determine the confidence of the final subsumption relation. The resulting list contains triples (keyword i, keyword j, confidence), denoting that keyword i subsumes keyword j with the given confidence. As the PageRank scores are power-law distributed for power-law distributed graphs (and co-occurrences are typically also power-law distributed), the tail of the PageRank score vector comprises many keywords which are only very weakly related to the keyword, on whose neighborhood we biased the PageRank computation. Hence, we can safely apply tail cutting in step 1 and keep only those elements in the sorted PageRank score vector, which accumulate 80% of the overall score in the score vector (following the well-known 80-20 rule). In the third step, we apply the following rules to filter too weak relations and determine the confidence of the remaining subsumption relations: 1. If neither keyword i is among the top-Xj elements of the PageRank score vector of keyword j nor keyword j is among the top-Xi elements of the vector of keyword i, then both are assumed to be too weakly related and are deleted from the list of subsumption candidates. 2. If the subsumed keyword is among the top-X elements of the score vector of the subsuming keyword, then we set the confidence in the subsumption relation to ‘low’ (weak relation). 3. If both keywords are among the top-X elements of both score vectors, then we set the confidence in the subsumption relation to ‘high’ (strong relation). 3.3 Part III: Creating GrowBag Graphs In the third and final part, the Semantic GrowBag algorithm finally uses the hidden related keywords of i as ‘seed nodes’ and ‘grows’ the set of nodes to create a specific GrowBag graph for keyword i using the following steps: 1. Add those keywords as nodes that subsume the latent related keywords of i directly. 2. Add recursively all keywords as nodes that are subsumed by already collected keywords. 3. Add all relations as edges where both involved keywords have already been collected. Use different edge visualizations to account for the ‘strength’ of the relation (dashed lines for weak relations and two-headed arrows for strong relations). The intention is to visualize only the most important related neighbors and the keywords subsumed by them to limit the size of the graph.


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4 Use Case and Experimental Evaluation with Practical Data This section explains the Semantic GrowBag algorithm in detail along one use case for the start keyword ‘RDF’ and additionally shows how the GrowBag graph for ‘RDF’ changes over time. We used a subset of the computer science publications listed in DBLP and extracted the author keywords from the web, post-processed them using acronym replacement and Porter stemming [20] and removed those, which are mentioned less than five times, resulting in 13, 200 keywords. The relation DocumentID → keywords comprises about 500, 000 entries for 93, 000 publications.3 4.1 Creating a GrowBag Graph for ‘RDF’ This use case shows how to find the GrowBag graph for the keyword ‘RDF’ for the period 2001–2005. The description follows the three main parts of the algorithm to (1) create a co-occurrence matrix including higher order co-occurrences, (2) find relations between keywords, and (3) create a GrowBag graph for the keyword ‘RDF’. In out GrowBag demonstrator,4 all graphs are periodically updated (so the version in the Web might differ from the graphs presented here). Part I: Top-X and Biased PageRank. After creating the co-occurrence matrix M , the algorithm first extracts the corresponding ‘RDF’ row of M , comprising the weighted co-occurrences of ‘RDF’ (using the ICF as weight) with all other keywords. This matrix row is sorted according to the matrix values (cf. left part of Table 1; the plain co-occurrence values are shown for comparison. In this example, the direct neighbors of ‘RDF’ are ‘RDF’ itself (the start keyword is included by definition), ‘Semantic Web’ and ‘Metadata’ as they accumulate P = 20% of the total sum of matrix values of all keywords in that list.5 Table 1. Direct neighbors (left) / the PageRank score vector (right) for ‘RDF’ (top-X = 3) Rank 1 2 3 4 5 6 7 8 9 10 ... 3 4 5

Keyword RDF Semantic Web Metadata XML Annotation Ontology DAML RDF Schema DAML+OIL OWL ...

m(j, i) cooc(i, j) 259.8 55 112.1 31 55.3 15 41.4 15 26.3 6 26.1 8 23.1 4 20.6 3 18.0 3 16.0 3 ...

Rank 1 2 3 4 5 6 7 8 9 10 ...

Keyword Semantic Web Metadata RDF Ontology XML Web Service Information Retrieval Data Mining Clustering Annotation ...

Score 828.1 755.0 746.4 127.1 120.2 74.6 50.2 49.3 49.3 49.0 ...

Titles, authors, citations etc. are planned to be included in future versions. http://dblp.l3s.de/GrowBag This heuristics to find P was confirmed by manual inspection of a large set of resulting graphs but is subject of further research.

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Afterwards, the algorithm uses PageRank (biasing to 100% on the three direct keywords of ‘RDF’) to find a new ranking. The resulting PageRank score vector includes higher-order co-occurrences, which are not reflected in the sorted co-occurrence vector. Practically speaking, the main objective of the first part is to see whether the start keyword ‘RDF’ itself remains the ‘top keyword’ in the score vector after running the Biased PageRank or if other keywords are more relevant and ‘overtake’ (in the sorted co-occurrence vector, the start keyword stays always on top). As shown in the right part of Table 1, indeed ‘Semantic Web’ and ‘Metadata’ achieve a higher score than ‘RDF’ and are candidates for subsuming ‘RDF’. Part II: Deriving Relations. The basic idea of the second part to identify ‘subsumption’ relations between ‘RDF’ and its direct neighbors is to do a pairwise comparison of the scores of two vectors: the PageRank score vector of ‘RDF’ and of its direct neighbors (cf. Table 2). Table 2. PageRank score vectors for ‘Semantic Web’ (left; X = 8) & ‘Metadata’ (right; X = 16) Rank 1 2 3 4 5 6 7 8 9 10 ...

Keyword Semantic Web Ontology XML Web Service RDF Metadata Description Logics OWL Data Mining Security ...

Score 407.7 373.0 358.4 330.6 311.4 303.5 288.5 284.3 47.8 45.6 ...

Rank 1 2 3 4 5 6 7 8 9 10 ...

Keyword Metadata XML Semantic Web Digital Libraries Ontology Interoperability Annotation RDF web OAI ...

Score 262.6 125.1 207.1 199.5 197.5 172.3 171.8 167.1 151.1 149.7 ...

For the direct neighbor ‘Semantic Web’, the score of ‘RDF’ is lower than the score of ‘Semantic Web’ in both the PageRank list of ‘Semantic Web’ (cf. Table 2 (left)) and the PageRank list of ‘RDF’ (cf. Table 1 (right)). Hence, ‘RDF’ is defined to be subsumed by ‘Semantic Web’ (RDF achieves a sufficiently high score in both lists not to be affected by tail cutting). Since ‘RDF’ is among the top-X = 8 elements of the PageRank score vector of ‘Semantic Web’, the confidence in this relation is defined as ‘strong’. Analogously, ‘RDF’ is found to be subsumed by ‘Metadata’. Part II is repeated to find subsumption relations between all pairs of keywords. Part III: Combining the Relations into a Graph. For the final graph, the set of keywords N related to ‘RDF’ and the corresponding edges between the keywords in N have to be found. An excerpt of the resulting graph when starting from ‘RDF’ is depicted in Fig. 3: To determine N , the Semantic GrowBag algorithm includes all direct neighbors of ‘RDF’ in N as initial ‘seed’, i.e., the start keyword ‘RDF’ itself (depicted as box with a black background), and the keywords ‘Semantic Web’, and ‘Metadata’ (grey boxes).

The Semantic GrowBag Algorithm: Automatically Deriving Categorization Systems semantic web (1)

ontology (4)

edge sition 1)

process (498)

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metadata (2)

web data extraction (790)

DAML-S (489)

representation (352)

markup (255)

RDF (3)

forest (878)

cataloging (678)

<start tag>

document classification (780)

information model (415)

application server (568)

mereology (2688)

semantic grid (720)

semantic web services (101)

rules (85)

DAML+OIL (40)

() weak relation

Knowledge Grid (1039)

declarative (325)

strong relation

Fig. 3. Excerpt of the GrowBag graph for ‘RDF’ in the period 2001–2005

This set is grown by recursively collecting all subsumed keywords in N (transparent boxes). Finally, the immediate ‘parents’ of the direct neighbors are added to the final set N to put these direct neighbors into their immediate context (which does not add keywords to the graph in this example). To connect the nodes, all known subsumption relations involving keywords from N are added. Two additional pieces of information are also shown in the graph: First, the Semantic GrowBag algorithm additionally provides a ‘confidence’ for an edge that depends on the underlying data. Second, all keywords in the graph are also associated with their rank in the PageRank score vector of the start keyword ‘RDF’, which gives evidence on how closely related the keyword is to ‘RDF’. As an example, ‘ontology’ (rank 4; sibling of ‘RDF’) has a closer relation to ‘RDF’ than ‘web data extraction’ (rank 790; also a sibling of ‘RDF’). 4.2 Graph Development over Time Whereas facets are usually considered to be static, the Semantic GrowBag algorithm can also show the development of the categorization over time for our input data. The previous figure depicted the GrowBag graph using all documents in the main period of metadata (4) semantic web (1)

XML (2)

ontology (6)

semantics (29)

<start tag>

RDF (3) Data model (37)

hypertext (249)

rules (31)

matchmaking (55)

DAML+OIL (16)

DAML (5)

KAoS (30)

Fig. 4. GrowBag graph for ‘RDF’ (2002-2003)

() weak relation strong relation

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‘RDF’ (2001-2005). In contrast, figures 4 and 5 show the relations restricted to 2002– 2003 and 2003–2004 respectively6. In 2002/03, for example, ‘Semantic Web’ subsumes ‘DAML’ whereas in 2003/04, ‘Semantic Web’ subsumes ‘OWL’. Very interesting is the relation between ‘Semantic Web’ and ‘Alloy’ in 2003/04: Alloy is a modelling language from the Formal Methods community, that has been used to validate Semantic Web ontologies for consistency. semantic web (1) information integration (41)

alloy (585)

semantic grid (389)

DAML-S (295)

knowledge representation (11)

representation (266)

metadata (3)

DAML+OIL (93)

web intelligence (961)

declarative (141)

OWL (19)

XML (2)

RDF (4)

rules (35)

electronic contracts (172)

Fig. 5. GrowBag graph for ‘RDF’ (2003-2004)

4.3 Comparison with the Baseline Approach In general, the quality of categorization systems is a very subjective issue and can hardly be evaluated automatically. One objective of the Semantic GrowBag approach is finding additional relations between keywords based on higher-order co-occurrences. Hence, we compared the number of relations found by our approach with the relations found using the baseline approach [12] (cf. Sect. 2, to which we also applied the ‘tail cutting’ to eliminate too shallow relations). Table 3 shows the number of found relations in all GrowBag graphs and for the baseline approach for three different periods. Table 3. Identified relations and overlap for 1996-2000, 2001–2005, and 2004–2005 Approach GrowBag Baseline 51% Baseline 55% Baseline 60% Baseline 70% Baseline 80% Baseline 100% 6

2001–2005 1996-2000 2004-2005 Relations Overlap Relations Overlap Relations Overlap 2150 (929) n.a. 2494 (988) n.a. 853 (413) n.a. 2297 96 (63) 2101 414 (168) 1427 63 (35) 2297 80 (50) 2101 394 (152) 1427 59 (31) 1793 60 (41) 1859 334 (120) 1245 41 (18) 727 29 (18) 605 130 (49) 458 19 (8) 324 8 (7) 352 58 (20) 232 3 (2) 260 5 270 40 215 1

We coupled the data from two years, since the data was too sparse in single years. We omitted the children of XML for space reasons.


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For the originally proposed 80% threshold in the baseline approach, only about 230 − 350 relations are found (15 − 30% compared to GrowBag), of which 8 − 58 overlap with the relations identified by our approach. Hence, our approach finds additional relations using higher-order co-occurrences compared to the original baseline approach, which assumes that subsuming keywords and the keywords they subsume are used together pretty often (e.g., > 80%) to annotate one resource. This is true for automatically extracted keywords from full text, but in general happens less often for manually assigned keywords. Furthermore, most of those relations being found by both approaches are actually ‘strong’ relations as found by the Semantic GrowBag scheme (shown in parentheses in Table 3). This indicates that the distinction between ‘strong’ and ‘weak’ relation is reasonable, though there is a high variance between the different periods. As an alternative way to increase the identified number of relations in the baseline approach, the threshold could be decreased to 51 − 60% (cf. Table 3). However, the overlap in the identified relations between GrowBag and the baseline approach remains still very small. We also examined the depth of the hierarchies created by GrowBag and the baseline approach with 60% and found that less than 10% of the nodes have a depth larger than one (i.e., have ‘grand-children’ nodes) with an average depth of 1.1 as opposed to 22% and an average depth of 1.3 in the GrowBag approach. Hence, the introduction of higher-order co-occurrences helps in creating deeper hierarchies as expected. While this section does not allow to judge the quality of the relations found by our approach, it shows that the same results cannot be achieved with a (simpler) system based on first-order co-occurrences only. 4.4 The Top-X Threshold Finding an appropriate top-X is important for high quality graphs. In this context, the threshold P = 20% for the accumulated sum of the PageRank scores was determined empirically for our specific data set. We varied this threshold between 10% and 30%, but already in the 15% case very many keywords have two or less keywords as direct neighbors, so that too few PageRank lists can be computed. In the 25% case, the maximum size of the top-X list becomes too high (up to 49 compared to 34 in the 20% case). This threshold P will be subject of further research and always has to be adapted to the underlying corpus. This is because it influences the quality of the identified relations and depends on characteristics of the particular object collection such as the average number of keywords per object, the average number of co-occurring keywords, etc.

5 Summary and Future Work In this paper we presented the Semantic GrowBag approach for the automatic creation of hierarchical categorization systems for usage in topic facets. Exploiting existing author keyword annotations, we have strong evidence that the automatically derived facets generally are indeed semantically meaningful. Even at this preliminary stage of applying the algorithm to real world collections (which are often not yet thoroughly annotated) the existing annotations allow to get sufficiently good facets for result presentation. Moreover, we have shown that also the evolution of topics over time can be derived

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and may provide an added value for collection browsing. This benefit for result presentation has already been demonstrated in the FacetedDBLP prototype [6] that also can be accessed online7. It provides a faceted view on the DBLP data using GrowBag graphs to create topic facets (with respect to a chosen time interval). As future work, we want to further improve the quality of the topic facets, e.g., using a more sophisticated detection of the direct neighbors (i.e., the top-X). Furthermore, we envision a change detection scheme to automatically detect keywords in quickly changing environments. We have also started to look at different input data sets, such as the Medline Database, or data sets stemming from collaborative tagging, which in general require a much stronger pre-processing stage to clean the corpus of keywords/tags.

Acknowledgments This work was partially funded by the European NoE Knowledge Web and the EmmyNoether Program of the German Research Foundation DFG. The authors gratefully acknowledge the many fruitful discussions with our colleagues.

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http://dblp.l3s.de


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13. Schütze, H.: Automatic word sense discrimination. Comput. Linguist. 24(1), 97–123 (1998) 14. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University (1998) 15. Jeh, G., Widom, J.: SimRank: A Measure of Structural-Context Similarity. In: Proc. of the SIGKDD conference (2002) 16. Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990) 17. Hofmann, T.: Probabilistic latent semantic indexing. In: Proc. of SIGIR conference, pp. 50– 57 (1999) 18. Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. ACM Press /Addison-Wesley (1999) 19. Langville, A., Meyer, C.: Deeper inside pagerank. Internet Mathmatics 2(1), 335–380 (2004) 20. Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

Ontology-Based Question Answering for Digital Libraries Stephan Bloehdorn1, Philipp Cimiano1 , Alistair Duke2 , Peter Haase1 , Jörg Heizmann3, Ian Thurlow2, and Johanna Völker1 1

Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany

2 British Telecom, Adastral Park, Ipswich IP5 3RE, UK

3 ontoprise GmbH, Amalienbadstr. 36, D-76227 Karlsruhe, Germany

Abstract. In this paper we present an approach to question answering over heterogeneous knowledge sources that makes use of di«erent ontology management components within the scenario of a digital library application. We present a principled framework for integrating structured metadata and unstructured resource content in a seamless manner which can then be flexibly queried using structured queries expressed in natural language. The novelty of the approach lies in the combination of di«erent semantic technologies providing a clear benefit for the application scenario considered. The resulting system is implemented as part of the digital library of British Telecommunications (BT). The original contribution of our paper lies in the architecture we present allowing for the nonstraightforward integration of the di«erent components we consider.

1 Introduction In the last decade, Digital Libraries [1] have emerged as a standard means for accessing published resources maintained electronically by libraries. Many digital libraries have evolved from traditional libraries and concentrated on making their information sources available to a wider audience. Today, many companies maintain their own digital libraries, and research and development for digital libraries now includes processing, dissemination, storage, search and analysis of all types of digital information. In contrast to physical libraries, digital libraries enable concurrent access at any time without physical boundaries. As such, digital libraries can be regarded as indispensable tools for today’s knowledge workers. Digital libraries have always been an appealing playground for innovative computer science solutions. To name just a few examples, cross-referencing functionalities, document digitalization and optic character recognition (OCR), improved information retrieval techniques and recommender functionalities have changed the way we interact with digital libraries far beyond their basic functionalities. So far, functionalities of digital libraries are typically implemented in a specific context and are tuned to meeting the requirements of a specific audience. Currently missing are generic methods for the smooth integration of content stemming L. Kovács, N. Fuhr, and C. Meghini (Eds.): ECDL 2007, LNCS 4675, pp. 14–25, 2007. c Springer-Verlag Berlin Heidelberg 2007

Ontology-Based Question Answering for Digital Libraries

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from di erent sources and for a flexible framework for implementing new functionalities. Further, fine-grained access to resources (resource retrieval) and facts (fact retrieval) in the form of structured queries combining fine-granular metadata and fulltext content is typically not provided. In this paper we show that with the help of semantic technologies we can bridge this gap. In particular, ontologies o er a generic solution to the problem of integrating various sources. The documents in the knowledge sources are annotated and classified according to the ontology. Hereby, an ontology is essentially a logical theory conceptualizing relevant aspects of an underlying domain, in our case the domain of publications. Our ontology model consists of concepts organized hierarchically in terms of subsumption as well as of (binary) relations together with appropriate domainrange restrictions. The ontological metadata can then be exploited for advanced knowledge access, including navigation, browsing, and semantic search. Advanced semantics-based mining technology can extract fine-grained metadata from articles contained in the digital library. Finally, current reasoning techniques allow to answer structured queries to access full-text content as well as fine-grained metadata from articles from di erent sources in a uniform way. In this paper, we present a principled framework for integrating digital library knowledge sources as well as facts extracted from the content under consideration by means of an ontology-based digital library system that can be used for several adaptions of the standard digital library scenario. As an example, we then present a system that allows the posing of structured natural language queries against the digital library with a welldefined semantics provided by the underlying ontology. The resulting system has been implemented as part of a case study with the Digital Library of British Telecommunications plc (BT) within the EU IST integrated project Semantic Knowledge Technologies (SEKT)). It combines a variety of tools for natural language question interpretation, information extraction, query answering and reasoning which are glued together by ontology-based knowledge representation formalisms and a corresponding ontology and metadata management system. This paper is organized as follows: In section 2 we introduce a scenario which will serve as a running example throughout this paper. From this scenario, we derive a number of requirements for an ontology-based digital library system. In section 3, we present the architecture and the components of our system. In section 4 we refer back to the earlier requirements and describe how they are addressed and handled in our implementation. Section 5 discusses related work while Section 6 summarizes the main contribution of the paper.

2 The Scenario In this section, we present a short scenario which will be used as a running example throughout this paper: Scenario (BT Digital Library). Bob works as technology analyst for British Telecom. His daily work includes research on new technological trends, market developments as well as the analysis of competitors. Bob’s company maintains a digital library that gives access to a repository of internal surveys and analysis documents. The company also has a license with an academic research database which is accessed via a separate

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interface. Depending on his work context, Bob uses the topic hierarchies, the full-text search functionalities or metadata search facilities provided by the two libraries to get access to the relevant data. However, Bob is often annoyed by the di«ering topic hierarchies and metadata schemes used by the two libraries as well as by a cumbersome syntax for metadata queries. Questions which Bob might be interested in could include: 1. 2. 3. 4. 5. 6.

What articles were published by William Arms in ”Communications of the ACM”? Who wrote the book ”Digital Libraries”? What article deals with Grid Computing? What are the topics of ”The future of web services”? Which conference papers were classified as ”emerging technology trends 2007”? Which articles are about “Intellectual Capital”?

The user Bob in this scenario can be seen as a typical example of a knowledge worker who is using digital libraries for everyday research tasks. The scenario also points to a number of deficiencies of many of the current interfaces to digital libraries. From the initial scenario and the above questions, we derive the following requirements: Support for Structured Queries Against Metadata and Documents: With current interfaces to Digital Libraries, users either pose keyword-based queries on document fulltexts or abstracts or they use metadata search facilities to perform document retrieval. However, question 1 requires as answers articles which fulfill the condition of having been published by William Arms in a certain journal, i.e. Communications of the ACM, and thus a structured query against the metadata of the articles. Question 2 requires the author of a certain publication, i.e. the book with title ”Digital Libraries”. This shows that in general we are interested in receiving as answers facts from the knowledge base rather than only relevant documents. We thus require the capability to pose structured queries to the underlying digital library which can be evaluated against the articles’ metadata as contained in the knowledge base. For metadata queries, current interfaces either o er preselected attribute fields (which are interpreted as conjunctive queries over a attribute-specific fulltext search) or they require some kind of formal query language. Integration of Heterogeneous Knowledge Sources: In general, answering questions as the above might however require uniform access to di erent sources or repositories. However, a common limitation is that di erent providers of digital libraries use di erent taxonomies and di erent metadata schemes to describe the stored content, which requires a user to switch between di erent representations depending on the backend system searched. A particular challenge is the integration of structured knowledge sources (e.g. document metadata) and unstructured sources (e.g. document fulltexts). We would like to access heterogenous knowledge sources via a single, unified interface that integrates di erent metadata schemes as well as di erent topic hierarchies. Automatic content extraction and classification: Questions 3 and 4 might require finegrained access to the topics of articles. Hereby, with topics we mean items of some classification scheme to which documents are related. Though in many cases articles are classified, we can not expect that all relevant categories are actually explicitly stated.


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Thus, some support for automatically capturing the content of new documents added to the library seems necessary. The content of the Digital Library is not static, but changes over time: New documents come in, but also documents may be removed from the document base. We here require means to automatically extract relevant knowledge and to classify the new content. Natural Language Interface: Finally, in order to provide intuitive access and not to impose the burden on the user of learning a formal query language, we would like to allow the user to use an intuitive query language, ideally arbitrary natural language queries. In our running scenario, Bob should thus be able to ask the questions directly in the way they are stated in the scenario above. All of the above requirements can, of course, be tackled by ad-hoc modifications of the interfaces. The approach we take is, however, generic and can be flexibly extended. We have implemented an approach that enables the user to perform structured natural language queries against the information contained in the Digital Library. The semantics of the information and the user queries is defined by an underlying ontology. As a result, users are able to ask queries such as ”What article deals with Grid Computing?”, i.e. a query that allows for relating di erent knowledge sources and that does not only allow to return documents, but structured answers to the query.

3 Architecture and Components In this section we discuss the overall architecture of the proposed digital library system. The architecture – as shown in Figure 1 – consists of the following main components: – The Knowledge Portal is the user interface to the digital library. The user interacts with the portal by asking queries in natural language. The underlying transformation and query answering processes are completely transparent to the user. Figure – The Query Translation component translates the natural language queries into structured logical queries against the ontology. This translation relies on a deep parsing of the questions using a lexicon that describes the possible lexical realizations of the elements in the ontology. The resulting logical queries are expressed in SPARQL [2], a query language standardized by the W3C for the Semantic Web. – The Query Answering component manages the integrated ontologies and performs the answering of SPARQL queries against the knowledge sources. Extensions can be integrated that enable additional search functionalities at query time, e.g. for online-text classification of content. The query results are tuples of variable bindings that satisfy the query. – The Knowledge Base of the digital library consists of a number of heterogeneous knowledge sources, partially structured in the form of metadata and topic hierarchies, but largely unstructured in the form of fulltext documents. All these data sources are integrated using an ontology. The Ontology Learning component is used to automatically extract structured ontologies from the unstructured text documents in the library. This allows the integration of the text documents with the other data sources such that they can be queried in a uniform way.

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Fig. 1. Conceptual Architecture of the Application

In the following, we will discuss the individual components in more detail. While the architecture is generic in principle, we illustrate it using the components used for the implementation of the BT Digital Library. 3.1 Knowledge Base and Ontology As shown in the bottom of Figure 1, the knowledge base of the digital library comprises a number of di erent knowledge sources. In the BT Digital Library, it consists of databases with bibliographic metadata, topic hierarchies, such as INSPEC1 , but also unstructured sources such as full text documents with di erent formats. All these heterogeneous knowledge sources are integrated using a common ontology, which is based on a designed general ontology, which we call PROTON (PROTo ONtology)2. Within PROTON, we adapt a layered approach: The classes in this ontology are a mixture of very general classes, e.g. , , , , and classes which are more specific to the world of business, e.g. , , 1 2


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. Finally, the ontology contains domain specific aspects, including classes relating to the specifics of the library, e.g. to the particular information sources available. Within PROTON there is a class such that each individual topic is an instance of this class. However, frequently a topic will be a subtopic of another topic, e.g. in the sense that a document ’about’ the former should also be regarded as being about the latter. The hierarchy of topics is modeled using a special relation ubTopic. This relationship is defined to be transitive, in the sense that if A is a subtopic of B and B is a subtopic of C, then A is also a subtopic of C. The structured information sources are integrated using a mapping of the underlying structures to the ontology. As a simple example, a database table describing persons would be mapped to the class in the ontology along with its properties, such as name aÆliation, etc. The mapping formalism [3] also supports more complex mappings that establish correspondences between conjunctive queries over the sources and the ontology. For the unstructured sources – such as fulltext documents – the mapping is not as direct. Instead, we obtain structured ontologies from the unstructured sources with the help of the ontology learning tool Text2Onto [4], as explained in the following. 3.2 Ontology Learning Ontology Learning aims at learning and extending ontologies on the basis of textual data. In our system, ontology learning is used to dynamically extract new topics, concepts and relations in the underlying document collection. For this purpose, we exploit Text2Onto, a framework for ontology learning and data-driven ontology evolution [4]. It relies on a combination of natural language processing and machine learning techniques for extracting ontologies from unstructured textual resources. In particular, it implements algorithms for learning the following ontological primitives: concepts and instances as well as subconcept, subtopic, instance-of and arbitrary binary relations between concepts. The algorithms implemented in Text2Onto build on a variety of techniques from information retrieval (e.g. statistical measures such as tf.idf for term extraction), natural language processing (e.g. lexico-syntactic patterns for extracting subconcept and instance-of relations) as well as machine learning (e.g. clustering for learning concepts hierarchies, association rules for extracting binary relations), etc. To illustrate the ontology learning process consider the following excerpt from a digital library document about collaborative development environments: To support remote authoring of Web pages and file contents, as well as remote source code access, GForge uses several network protocols, including SSH, SFTP, CVS pserver, and FTP. Given a lexico-syntactic pattern matching a sequence of noun phrases such as NPconcept , including NP1instance . . . NPninstance Text2Onto would conclude from this sentence that SSH, SFTP, CVS pserver and FTP are instances of network protocol. If the user later asks for documents about network protocols, the learned concept instantiation will enable a reasoner to infer that a document dealing with SSH might be relevant for the user – even if the term network protocol is not explicitly mentioned in the text. We apply the algorithms to extract the above mentioned primitives from each of the relevant information spaces in the digital library, and merge the resulting ontologies. Most importantly, Text2Onto keeps a document pointer to the document where a certain ontological primitive was extracted from. This allows fine-grained queries to be posed

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to the document collections by asking for the topics, concepts, instances, as well as di erent types of taxonomic and non-taxonomic relations occurring in a document. By the integration of Text2Onto we could for example answer questions like: What network protocols are talked about in the article ”The Future of Web Services”? or What articles are about ”Intellectual Capital”?. 3.3 Query Answering The integrated ontology is managed by the KAON2 ontology management system3 , which acts as the query answering component. As mentioned before, the queries are logical conjunctive queries against the ontology. Query answering amounts to a reasoning process over the knowledge sources according to the semantics of the underlying ontology language OWL. To represent the conjunctive queries, we here rely on SPARQL as the query language, an ontology query language standardized by the W3C [2]. The query answering is not a mere retrieval of explicitly stated facts (as in a conventional database), but involves a deduction of answers over the knowledge base [5]. As an example, consider the following SPARQL query which asks for articles that are associated with the topic “Intellectual Capital”?: !"##$#% &'( )* !"##$#% ' & +* ) ) , -,./0))&) )/1

For the answering of queries, we follow a virtual integration approach that does not require a full integration of all knowledge sources: The actual data still resides in the individual knowledge sources, and the mapping between the ontology and the knowledge sources is only used at runtime to retrieve the facts that are actually relevant for answering a query. In a query, predicates can be used that require access to di erent knowledge sources. The evaluation of these predicates is automatically pushed down to the respective knowledge sources, e.g. as a relational query in the case of a relational database, or to a fulltext index in the case of the fulltext match predicate over the topic hierarchy contained in the above query. In Section 3.5, we discuss the evaluation of a special predicate for the classification of text documents. At last the result set is processed and sent back to be displayed by the BT knowledge portal. 3.4 Query Translation from Natural Language ORAKEL [6] is a natural language interface which translates natural language queries to structured queries formulated with respect to a given ontology. This translation relies essentially on a compositional semantic interpretation of the question guided by two lexica: a domain-specific and a domain-independent lexicon. As the name suggests, the domain-independent lexicon is a priori encoded into the system and captures the domain-independent meaning of prepositions, determiners, question pronouns etc., which typically remain constant across domains. The domain-specific lexicon needs to 3


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Fig. 2. Overview of the ORAKEL system

be created by a lexicon engineer who is assumed to create the lexicon in various cycles. In fact, ORAKEL builds on an iterative lexicon development cycle in which the lexicon is constantly updated by the lexicon engineer on the basis of the questions the system failed to answer so far. The end users then are able to directly interact with the BT digital library portal by accessing the library data via natural language questions, which are translated into SPARQL queries by the ORAKEL system. The underlying mechanism however is hidden from the users - the only thing users need to do is to input the query just as their normal questions and then get the result from the portal. The obvious advantage of using such a natural language interface is that users do not have to learn nor struggle with a formal query language, while they can still benefit from the possibility to pose structured queries. Further, ORAKEL was also modified to process quoted text by matching it against a text index of the metadata in the database using a special purpose predicate match. The question ”What articles are about “Intellectual Capital”?” is translated into the SPARQL query presented above. Figure 2 gives an overview of the ORAKEL system, which has been designed in a flexible manner allowing the system’s target query language to be easily replaced. As the architecture described here relies on the KAON2 system as inference engine and knowledge repository, ORAKEL was adapted to generate SPARQL queries. Further, a lexicon was generated for the Proton ontology, which specifies the possible lexical representations of the ontology elements in the user queries. 3.5 Text Classification Often query answering requires the evaluation of predicates whose extensions need to be determined using special purpose algorithms at query time. This is for example the case if the text documents need to be classified against parameters that are provided at query time by the user. Automated text classification [7] is a standard tool for adaptive

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categorization of textual data. In essence, given a collection of positive and negative examples, machine learning techniques are used to discover patterns in the text that will help in the categorization of unseen documents in the future. For topics for which no explicit library classification exists, e.g. personalized interests or emerging topics, one can train and use such a classification module. Of course, the classification accuracy for text mining tasks will depend on many factors such as representation, training algorithm, number of training documents and parameter setting. We have used a simple approach that has been shown to perform well in practice, namely the bag-of-words representation together with Support Vector Machine (SVM) classification. While training is performed oine, the resulting models can be simply integrated into the system by means of the Built-In mechanism. As a result, the special-purpose predicate “classifiedas” is contained in the ontology but its extension is evaluated at query time against the document fulltext and the stored model(s)4.

4 Scenario Revisited The architecture described in the previous section has been implemented and deployed at BT. In contrast to the previously existing system, the resulting question answering prototype does not only allow for asking questions about existing metadata in the BT library, but also about topics found by Text2Onto as well as to invoke the automatic text classification system at runtime. The net result is that a variety of queries about authors, topics, about documents etc. could be answered successfully against the BT ontology and database. The questions introduced in section 2 are examples of supported natural language queries. We will revisit two of these questions. Consider the question What articles were published by ”William Arms” in ”Communications of the ACM”?. The corresponding SPARQL query would look as follows: !"##$#% &'( )* !"##$#% ' &(& * !"##$#% &'& ) * , ) ) -.22)) ( 331 , ) ) , -.22& (4331 5

As another example, consider the question “Which conference papers are classified as ”emerging technology trends 2007?”. By means of ORAKEL and a small set of rules, the question would result in the following SPARQL query, which would use the SVM classification on document abstracts of documents of type article and the (previously trained) SVM model “emerging technology trends 2007”: !"##$#% &'( )* !"##$#% ' &( * 6(7( !8!)-.3 !! )! "##931 :0 !*# 5 4

The underlying module for text classification and corresponding training is part of the TextGarden library. See for further information.


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Table 1. Results for the di«erent iterations Iteration Recall (avg.) Precision (avg.) 1 42% 52% 2 49% 71% 3 61% 73%

Furthermore, an evaluation of the system was carried out with BT and other users. A primary goal was to evaluate the performance of the natural language question answering over several iterations of the ontology lexicon. The lexicon was constructed in three iterations: one initial iteration of 6 hours and 2 follow-up iterations of each 30 minutes in which the questions not answered by the system were examined. The end users received written instructions describing the conceptual range of the knowledge base, asking them to ask at least 10 questions to the system. In each of three iterations, 4 end users asked questions to the system and a graduate student updated the lexicon on the basis of the failed questions after the first and second round for about 30 min., respectively. The end users were also asked to indicate whether the answer was correct or not, which allowed for the evaluation of the system’s performance in terms of precision and recall. The results of the evaluation are presented in Table 1, which clearly shows that the second iteration performed much better than the first one, both in terms of precision and recall. In the third iteration, there was a further gain in recall with respect to the second iteration. Concluding, we can on the one hand indeed say that the precision of the system is fairly high. On the other hand, the lower recall is definitely compensated by a fallback strategy. In case the semantic understanding and answering of the query fails, our system resorts to standard information retrieval techniques to provide a number of relevant documents to the user’s query. This move thus makes the implemented system very robust. Overall, the application of our prototype showed that we could indeed improve the access to BT’s digital library by (i) integrating of data and sources using an inference engine (ii) precise question answering, (iii) exploitation of new topics detected by Text2Onto, as well as (iv) on-the-fly text classification functionality.

5 Related Work While traditional digital library systems such as e.g. DSpace 5 or e Prints6 have mostly focused on providing a generic infrastructure for storing digital content and metadata, the relation to Semantic Web technologies has been mostly restricted to metadata importexport functionalities. The actual combination of semantic technologies and Digital Libraries has only received increased attention in recent years. In the following, we discuss some related projects that apply Semantic Web technologies for digital libraries. An initiative that is in many aspects similar to our work, is the JeromeDL project7 [8], which employs Semantic Web technologies mainly for user management and 7 5 6

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personalized search within a digital library. In JeromeDL, full text content, bibliographic entries etc. are described with respect to the Jerome ontology. JeromeDL is distinguished by the extensive conceptualization and advanced search and personalization algorithms. However, in contrast to our approach, JeromeDL lacks any kind of knowledge extraction or natural language query functionalities. The SIMILE Project8 ([9]), aims at enhancing interoperability among digital assets, vocabularies, metadata and services. SIMILE tackles the challenge that collections can be distributed but should be queried in a uniform way. Semantic Web technologies, in particular the Resource Description Framework (RDF) [10] are used to tackle the challenge that collections can be highly distributed but need to be queried in a uniform way. While SMILE shares the interoperability-related aspects with our work, it does not go beyond this. Recently, some initiatives have emerged with the aim of moving from a content-centered organization of digital libraries towards more service-oriented architectures. The main idea of projects such as FEDORA project9 ([11]) is to support the whole digital content value chain from data creation, sharing, search and dynamic provision of appropriate services.

6 Conclusions We have presented an approach that combines a number of semantic technologies, including ontology management, ontology learning and reasoning, keyword-type search and text classification in order to allow the flexible and versatile answering of natural language questions on top of a digital library. The users are able to perform structured natural language queries against a variety of knowledge sources in an integrated manner with a well-defined semantics provided by the underlying ontology. The novelty of our system lies in the combination of di erent tools for natural language question interpretation, ontology learning, query answering as well as reasoning. Our experience showed that with reasonable e ort it is possible to apply semantic technologies to enhance a semantic library so that: (i) natural language questions can be answered precisely relying on standard (logical) querying techniques, (ii) topics can be automatically spotted over time and integrated into the system, and (iii) text documents can be classified on-the-fly given a user query. Though the integration of semantic technologies which have been developed recently at our institute (ORAKEL, Text2Onto, Text classification modules, KAON2) was far from straightforward mainly due to technical and infrastructure problems, the case study proved that semantic technologies are indeed mature enough to be integrated with reasonable e ort and deliver a clear added value. In this sense we can only encourage other people to venture out and experiment with semantic technologies in order to foster the exchange of experiences between the Semantic Web and Digital Library communities. Acknowledgements. This work was partially supported by the European Commission under contracts IST-2003-506826 SEKT, IST-2006-027595 NeOn, and IST-FP6026978 X-Media. 8 9


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References 1. Arms, W.Y.: Digital Libraries. MIT Press, Cambridge (2001) 2. Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF. W3C Working Draft (2007), available at !" ##$ 3. Haase, P., Motik, B.: A mapping system for the integration of OWL-DL ontologies. In: Hahn, A., Abels, S., Haak, L. (eds.) Proceedings of the first international ACM workshop on Interoperability of Heterogeneous Information Systems (IHIS’05), CIKM Conference, Bremen, Germany, November 4, 2005, pp. 9–16. ACM Press, New York (2005) 4. Cimiano, P., Völker, J.: Text2Onto - a framework for ontology learning and data-driven change discovery. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005) 5. Motik, B., Sattler, U., Studer, R.: Query answering for OWL-DL with rules. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 549–563. Springer, Heidelberg (2004) 6. Cimiano, P., Haase, P., Heizmann, J.: Porting natural language interfaces between domains – a case study with the ORAKEL system. In: Proceedings of the International Conference on Intelligent User Interfaces (IUI), pp. 180–189 (2007) 7. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34, 1–47 (2002) 8. Kruk, S.R., Decker, S., Zieborak, L.: Jeromedl - adding semantic web technologies to digital libraries. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 716–725. Springer, Heidelberg (2005) 9. Mazzocchi, S., Garland, S., Lee, R.: Simile: Practical metadata for the semantic web. XML.com (2006) 10. Manola, F., Miller, E.: Resource description framework (RDF) model and syntax specification (2004), !" 11. Payette, S., Staples, T.: The mellon fedora project: Digital library architecture meets xml and web services. In: Calzarossa, M.C., Tucci, S. (eds.) Performance 2002. LNCS, vol. 2459, pp. 406–421. Springer, Heidelberg (2002)

Formalizing the Get-Specific Document Classification Algorithm Fausto Giunchiglia, Ilya Zaihrayeu, and Uladzimir Kharkevich Department of Information and Communication Technology University of Trento, Italy {fausto,ilya,kharkevi}@dit.unitn.it Abstract. The paper represents a first attempt to formalize the getspecific document classification algorithm and to fully automate it through reasoning in a propositional concept language without requiring user involvement or a training dataset. We follow a knowledge-centric approach and convert a natural language hierarchical classification into a formal classification, where the labels are defined in the concept language. This allows us to encode the get-specific algorithm as a problem in the concept language. The reported experimental results provide evidence of practical applicability of the proposed approach.

1

Introduction

Classification hierarchies have always been a natural and effective way for humans to organize their knowledge about the world. These hierarchies are rooted trees where each node defines a topic category. Child nodes’ categories define aspects or facets of the parent node’s category, thus creating a multifaceted description of the objects which can be classified in these categories. Classification hierarchies are used pervasively: in conventional libraries (e.g., the Dewey Decimal Classification system (DDC) [8]), in web directories (e.g., DMoz [2]), in e-commerce standardized catalogues (e.g., UNSPSC [3]), and so on. Standard classification methodologies amount to manually organizing objects into classification categories following a predefined system of rules. The rules may differ widely in different approaches, but there is one classification pattern which is commonly followed. The pattern is called the get-specific principle, and it requires that an object is classified in a category (or in a set of categories), which most specifically describes the object. Following this principle is not easy and is constrained by a number of limitations, discussed below: – the meaning of a given category is implicitly codified in a natural language label, which may be ambiguous and may therefore be interpreted differently by different classifiers; – a link, connecting two nodes, may also be ambiguous in the sense that it may specify the meaning of the child node, of the parent node, or of both; – as a consequence of the previous two items, the classification task also becomes ambiguous in the sense that different classifiers may classify the same objects differently, based on their subjective opinion. L. Kov´ acs, N. Fuhr, and C. Meghini (Eds.): ECDL 2007, LNCS 4675, pp. 26–37, 2007. c Springer-Verlag Berlin Heidelberg 2007

Formalizing the Get-Specific Document Classification Algorithm

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In the present paper we propose an approach to converting classifications into formal classifications, whose labels are encoded in a propositional concept language. Apart from this, we present a classification model and show how the get-specific algorithm can be described in this model. We then show how the model and the algorithm can be encoded in the concept language, which allows us to fully automate document population in formal classifications through propositional reasoning. Note that by doing this, we eliminate the three ambiguities discussed above. In order to evaluate our approach, we have re-classified documents from several branches of the DMoz directory without any human involvement or an a priori created training dataset. The results show the viability of the proposed approach, which makes it a realistic alternative to the standard classification approaches used in Information Science. The remainder of the paper is organized as follows. In Section 2 we introduce the classification model, we show how the get-specific algorithm can be described in this model, and we identify the main problems peculiar to the algorithm. In Section 3 we show how classifications can be translated into formal classifications, how the get-specific algorithm can be encoded in the concept language, and how its peculiar problems can be dealt with in the concept language. In Section 4 we present and discuss evaluation results of our approach. In Section 5 we discuss the related work and, in particular, we compare our approach to that used in Information Science. Section 6 summarizes the results and concludes the paper.

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The Get-Specific Classification Algorithm

Classifications are hierarchical structures used for positioning objects in such a way, that a person, who navigates the classifications, will be facilitated in finding objects related to a given topic. To attain such organization of objects, in standard classification approaches, objects are manually classified by human classifiers which follow a predefined system of rules. The actual system of rules may differ widely in different classification approaches, but there are some generic principles which are commonly followed. These principles make the ground of the get-specific algorithm, described in the rest of this section. 2.1

Classifications and a Classification Model

To avoid ambiguity of interpretation, in Definition 1 we formally define the notion of classification; and in Figure 1 we give an example of a classification, extracted from the DMoz web directory and adjusted for sake of presentation. Definition 1. A classification is a rooted tree C = N, E, L where N is a set of nodes, E is a set of edges on N , and L is a set of labels expressed in a natural language, such that for any node ni ∈ N , there is one and only one label li ∈ L. We see the process of classification as a decision making procedure in which the classification tree is divided into a set of minimal decision making blocks. Each block consists of a node (called the root node of the block) and its child

28

F. Giunchiglia, I. Zaihrayeu, and U. Kharkevich

(1)

Top

(2)

Recreation

(7)

(3)

Collecting

(4)

Coins

(5)

Ancient

(10)

Ancient Coins

(12)

World Coins

(6)

Greek

(11)

Europe

(13)

Ancient Greece

(8)

Shopping

Antiques and Collectibles (9)

Coins

Fig. 1. A part of the DMoz web directory

Specific - General

nodes (see Figure 2). While classifying an object, the classifier considers these blocks in a top-down fashion, starting from the block at the classification root node and then continuing to blocks rooted at those child nodes, which were selected for further consideration. These nodes are selected following decisions which are made at each block along two dimensions: vertical and horizontal. In the vertical dimension, the classifier decides which of the child nodes are selected as candidates for further consideration. In the horizontal dimension, the classifier decides which of the candidates are actually selected for further consideration. If none of the child nodes are appropriate or if there are no child nodes, then the root node of the block becomes a classification alternative for the given object. The process reiterates and continues recursively until no more nodes are left for further consideration. At this point, all the classification alternatives are computed. The classifier then decides which of them are most appropriate for the classification of the object and makes the final classification choice. li

lj

...

ln

...

lm

Facets

Fig. 2. The decision making block

2.2

Modelling the Get-Specific Classification Algorithm

In this subsection we discuss the general principles lying behind the get-specific algorithm and we show how these principles can be implemented within the model introduced in the previous subsection. Particularly, we discuss how vertical and horizontal choices, as well as the final classification choice are made. – Vertical choice. Classification hierarchies are organized such that upper level categories represent more general concepts, whereas lower level categories represent more specific concepts. When the classifier searches for an


29

appropriate category for the classification of an object, she looks for the ones which most specifically describe the object and, therefore, when making a vertical choice, she selects a child node as a candidate if it describes the object more specifically than the parent does. For example, if a document about ancient Greek coins is classified in the classification from Figure 1, then node n6 is more appropriate for the classification than node n5 . When this principle is applied recursively, it leads to the selection of the category which lies as deep in the classification hierarchy as possible. The principle described above is commonly called the get-specific principle. Let us consider, for instance, how Yahoo! describes it: “When you suggest your site, get as specific as possible. Dig deep into the directory, looking for the appropriate sub-category.” [4] – Horizontal choice. Child nodes may describe different aspects or facets of the parent node and, therefore, more than one child node may be selected in the vertical choice if a multifaceted document is being classified. As a consequence of this, the classifier needs to decide which of the several sibling nodes are appropriate for further consideration. When one sibling node represents a more specific concept than another, then the former is usually preferred over the latter. For example, node n10 is more appropriate for the classification of ancient Greek coins than node n12 . As a rule of thumb, the horizontal choice is made in favor of as few nodes as possible and, preferably, in favor of one node only. We call the principle described above, the get-minimal principle. Consider, for instance, how DMoz describes it. “Most sites will fit perfectly into one category. ODP categories are specialized enough so that in most cases you should not list a site more than once.” [1] – Tradeoff between vertical and horizontal choices. The two principles described above cannot always be fulfilled at the same time. Namely, if the vertical choice results in too many candidates, then it becomes hard to fulfill the principle of minimality in the horizontal choice. In order to address this problem, a tradeoff needs to be introduced between the two requirements, which usually means trading specificity in favor of minimality. The following is an example of a tradeoff rule used in DMoz: “If a site offers many different things, it should be listed in a more general category as opposed to listing it in many specialized subcategories.” [1] – The final classification choice. When all classification alternatives are determined, the classifier confronts all of them in order to make her final classification choice. Note that now the choice is made not at the level of a minimal decision making block, but at the level of the whole classification. However, the classifier uses the same selection criteria as those used in the horizontal choice. For example, nodes n6 and n13 are more appropriate for the classification of documents about ancient Greek coins than node n11 .

30


2.3

Problems of the Get-Specific Classification Algorithm

As discussed in [10], there are several problems which are common to document classification algorithms. The problems are caused by the potentially large size of classifications, by ambiguity in natural language labels and in document descriptions, by different interpretations of the meaning of parent-child links, and so on. All these problems lead to nonuniform, duplicate, and error-prone classification. In addition to the problems discussed in [10], the get-specific algorithm has two peculiar problems, related to the two decision dimensions. We discuss these problems below on the example of a document titled “Gold Staters in the Numismatic Marketplace”, being classified in the classification from Figure 1. – Vertical choice: the “I don’t know” problem. The classifier may make a mistake because she does not (fully) understand the meaning of a child node or the relation of the document to that node, whereas the node is a valid candidate. For example, the classifier may not know that “Gold Stater” is a coin of ancient Greece and, therefore, will erroneously classify the document into node n5 , whereas a more appropriate node is n6 . – Horizontal choice: the “Polarity change” problem. The classifier may make a mistake when one of the sibling candidate nodes is more appropriate for further consideration than another, but a descendent of the latter is more appropriate for the classification than a descendant of the former node. For instance, the label of node n10 more specifically describes the document than the label of node n12 . Therefore, the classifier will choose node n10 only as a candidate and will finally classify the document in node n11 , whereas a more appropriate node for the classification is node n13 , a descendent of n12 .

3

Formalizing the Get-Specific Classification Algorithm

In this section we formalize the get-specific classification algorithm by encoding it as a problem expressed in propositional Description Logic language [5], referred to as LC . First, we discuss how natural language node labels and document descriptions are converted into formulas in LC . Second, we discuss how we reduce the problems of vertical, horizontal, and final classification choices to fully automated propositional reasoning. Finally, we show how the problems discussed in Section 2.3 can be dealt with in a formal way. 3.1

From Natural Language to Formal Language

Classification labels are expressed in a natural language, which is ambiguous and, therefore, is very hard to reason about. In order to address this problem, we encode classification labels into formulas in LC following the approach proposed in [10]. This allows us to convert the classification into a new structure, which we call Formal Classification (FC):


31

Definition 2. A Formal Classification is a rooted tree F C = N, E, LF where N is a set of nodes, E is a set of edges on N , and LF is a set of labels expressed in LC , such that for any node ni ∈ N , there is one and only one label liF ∈ LF . Note that even if LC is propositional in nature, it has a set-theoretic semantics. As proposed in [10], the interpretation of a concept is the set of documents, which are about this concept. For instance, the interpretation of concept Capital (defined as “a seat of government”) is the set of documents about capitals, and not the set of capitals which exist in the world. Below we briefly describe how we convert natural language labels into formulas in LC . Interested readers are referred to [10] for a complete account. Figure 3 shows the result of conversion of the classification from Figure 1 into a FC. 1. Build atomic concepts. Senses of nouns and adjectives become atomic concepts, whose interpretation is the set of documents about the entities or individual objects, denoted by the nouns, or which possess the qualities, denoted by the adjectives. We enumerate word senses using WordNet [17], and we refer to them as follows: pos-lemma#i, where pos is the part of speech, lemma is the word lemma, and i is the sense number in WordNet. 2. Disambiguate word senses. Irrelevant word senses are identified and corresponding to them atomic concepts are discarded. As proposed in [15], if there exists a relation (e.g., synonymy, hypernymy, or holonymy) in WordNet between any two senses of two words in a label (or in different labels on a path to the root), then corresponding to them concepts are retained and other concepts are discarded. If no relation is found, then we check if a relation exists between two WordNet senses by comparing their glosses [13]. 3. Build complex concepts. Complex concepts are built as follows: first, words’ formulas are built as the logical disjunction () of atomic concepts corresponding to their senses (remaining after step 2). Second, syntactic relations between words are translated into logical connectives of LC . For example, a set of adjectives followed by a noun group is translated into the logical conjunction () of the formulas corresponding to the adjectives and to the nouns; prepositions like “of” and “in” are translated into the conjunction; coordinating conjunctions “and” and “or” are translated into the logical disjunction (); words and phrases denoting exclusions, such as “except” and “but not”, are translated into the logical negation (¬). Before a document can be automatically classified, it has to be assigned an expression in LC , which we call the document concept, written C d . The assignment of a concept to a document is done in two steps: first, a set of n keyphrases is retrieved from the document using text mining techniques (see, for example, [22]); the keyphrases are converted to formulas in LC , and the document concept is then computed as the conjunction of the formulas. 3.2

The Algorithm

In the following we describe how we make vertical and horizontal choices, compute the tradeoff, and make the final classification choice in FCs.

32

F. Giunchiglia, I. Zaihrayeu, and U. Kharkevich (1)

Top

(2)

n-recreation#1 n-recreation#2

(7)

n-shopping#1

(3)

n-collecting#1

(8)

n-antique#2 n-collectibles#1

(4)

n-coin#1

(9)

n-coin#1

(5)

a-ancient#1 a-ancient#2

(6)

a-greek#1

(10)

(a-ancient#1 a-ancient#2) n-coin#1

(12) (a-world#1 n-world#1) n-coin#1

(11) n-Europe#1 n-Europe#2 n-Europe#3 (13)

a-ancient#1 n-Greece#1

Fig. 3. Formal Classification

– Vertical choice. A child node ni is a candidate, given that a document with concept C d is being classified, if the label of the node, liF , subsumes C d , i.e., if the following holds: C d liF . In formulas, if Nc is the set of child nodes in the block, then we compute the vertical choice V (C d ) as: V (C d ) = {ni ∈ Nc |C d liF }

(1)

If the vertical choice results in no candidates, then root node nr of the current block is added to the set of classification alternatives A(C d ): if |V (C d )| = 0 then A(C d ) ← A(C d ) ∪ {nr }

(2)

In Figure 4a we show an example of a situation when two child nodes n2 and n4 are selected for further consideration, and in Figure 4b we show an example of a situation when no child node can be selected.

stater

(2)

ancient

(1)

coin

(3)

certified

?

(4)

greek

stater

(2)

gold stater

(1)

coin

(3)

certified

(4)

ducat

?

(a)

(b)

Fig. 4. Vertical choice (“?” means no relation is found)

– Horizontal choice. Given the set of candidates V (C d ), we exclude those nodes from the set, whose label is more general than the label of another node in the set. In formulas, we compute the horizontal choice H(C d ) as: H(C d ) = {ni ∈ V (C d )|nj ∈ V (C d ), s.t. j = i, ljF liF , and ljF liF } (3) We introduce the last condition (i.e., ljF liF ) to avoid mutual exclusion of nodes, whose labels in the FC are equivalent concepts. For instance, two syntactically different labels “seacoast” and “seashore” are translated into two equivalent concepts. When such situation arises, all the nodes, whose labels are equivalent, are retained in H(C d ).


33

– The tradeoff. Whenever the size of H(C d ) exceeds some threshold k, the nodes of H(C d ) are discarded as candidates and root node nr of the block is added to the set of classification alternatives A(C d ). In formulas: if|H(C d )| > k then H(C d ) ← ∅ and A(C d ) ← A(C d ) ∪ {nr }

(4)

– The final classification choice. When no more nodes are left for further consideration, set A(C d ) includes all the classification alternatives. We compare them to make the final classification choice, but, differently from vertical and horizontal choices, we compare the meanings of nodes given their path to the root, and not their labels. We encode the meaning of node ni into a concept in LC , called concept of node [11], written Ci , and computed as: F if ni is the root of the F C l (5) Ci = iF li Cj if ni is not the root, where nj is the parent of ni Similar to how the horizontal choice is made, we exclude those nodes from A(C d ), whose concept is more general than the concept of another node in the set. In formulas, we compute the final classification choice C(A) as: C(A) = {ni ∈ A(C d )|nj ∈ A(C d ), s.t. j = i, Cj Ci , and Cj Ci } (6) The last condition (i.e., Cj Ci ) is introduced to avoid mutual exclusion of nodes with the same meaning in the classification hierarchy. For instance, two paths top/computers/games/soccer and top/sport/soccer/computer games lead to two semantically equivalent concepts. When such situation arises, all the nodes with the same meaning are retained in C(A). Computing Equations 1, 3, and 6 requires verifying whether the subsumption relation holds between two formulas in LC . As shown in [10], a problem expressed in LC can be rewritten as an equivalent problem expressed in propositional logic. Namely, if we need to check whether a certain relation rel (which can be , , ≡, or ⊥) holds between two concepts A and B, given some knowledge base KB (which represents our a priori knowledge), we construct a propositional formula according to the pattern shown in Equation 7 and check it for validity: KB → rel(A, B) 3.3

(7)

Dealing with Problems

Encoding a classification algorithm into a problem in LC allows it to avoid many problems, which are common to classification algorithms [10]. Particularly, since the problem is encoded in a formal language, there is no ambiguity in interpretation of classification labels, of edges, and document contents. Apart from this, since computation is performed by a machine, the problem of classification size becomes largely irrelevant. Finally, since the formal algorithm is deterministic, the classification is always performed in a uniform way. In Section 2.3 we discussed two problems, peculiar to the get-specific algorithm. Below we discuss what they mean in LC and how they can be dealt with.

34


– Vertical choice: The “I don’t know” problem. This problem arises when the specificity relation in Equation 1 cannot be computed while a human observes that it exists. The problem is caused by lack of background knowledge and it can be dealt with by adding missing axioms to the underlying knowledge base [12]. For instance, if we add a missing axiom which states that concept Stater (defined as “any of the various silver or gold coins of ancient Greece”) is more specific than concept Greek (defined as “of or relating to or characteristic of Greece . . . ”), then the algorithm will correctly classify document “Gold Staters in the Numismatic Marketplace” into node n6 in the classification shown in Figure 1. – Horizontal choice: The “Polarity change” problem. The problem arises when the label of node ni is more specific than the label of its sibling node nj (i.e., liF ljF ), but the concept of a ni ’s descendant node nk is more general than the concept of a nj ’s descendant node nm (i.e., Ck Cm ). In the simplest case, this problem can be dealt with by not performing the horizontal choice. In this case, both nk and nm will be in the classification alternative set for some document, and nk will then be discarded when the final classification choice is made.

4

Evaluation

In order to evaluate our approach, we selected four subtrees from the DMoz web directory, converted them to FCs, extracted concepts from the populated documents, and automatically (re)classified the documents into the FCs. We extracted document concepts by computing the conjunction of the formulas corresponding to the first 10 most frequent words appearing in the documents (excluding stop words). We used WordNet 2.0 [17] for finding word senses and their relations, and we used S-Match [11] for computing Equation 7. Parameter k for tradeoff computation was set to 2. In the evaluation we employ standard information retrieval measures such as micro- and macro-averaged precision, recall, and F1 [19]. In Table 1 we report dataset statistics and evaluation results for each of the four datasets. We performed a detailed analysis of the “Languages” dataset results (see Figure 5). In Figure 5a we show how precision and recall are distributed among nodes. Figure 5b shows how far (in terms of the number of edges) an automatically classified document is from the node where it was actually classified in DMoz. From Figure 5a we observe that about 40% of nodes in the “Languages” dataset have precision and recall equal to 01 . After manual inspection of the results, we concluded that this problem is caused by lack of background knowledge. For instance, 8 documents about Slovenian language were misclassified because there was no WordNet synset “Slovenian” defined as “the Slavic language spoken in Slovenia” and a hypernym relation of it with synset “Slavic language”. Figure 5b shows that about 20% of documents are classified in one edge distance from the node where they were originally populated, whereas 89% of them were 1

Precision for nodes with no documents was counted as 0.


35

Table 1. Dataset statistics and evaluation results Dataset

Nodes Docs

Photographya Beveragesb Mammalsc Languagesd b c

4 5 5 6

0.2218 0.4037 0.3145 0.4117

0.1871 0.4938 0.3014 0.4503

0.2029 0.4442 0.3078 0.4301

0.2046 0.3848 0.3854 0.4366

0.1165 0.3551 0.2677 0.4187

0.1485 0.3693 0.3159 0.4275

http://dmoz.org/Shopping/Photography/ http://dmoz.org/Shopping/Food/Beverages/ http://dmoz.org/Health/Animal/Mammals/ http://dmoz.org/Science/Social Sciences/Linguistics/Languages/Natural/Indo-European/

Number of nodes (%)

d

871 1456 574 1217

Mi-Pr Mi-Re Mi-F1 Ma-Pr Ma-Re Ma-F1

50 40 30 20 10 0

P=0, R=0

P=1, R=1

P=1, 0

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Research and Advanced Technology for Digital Libraries: 7th European Conference, ECDL 2003, Trondheim, Norway, August 17-22, 2003. Proceedings

Research and Advanced Technology for Digital Libraries: 12th European Conference, ECDL 2008, Aarhus, Denmark, September 14-19, 2008. Proceedings

Research and Advanced Technology for Digital Libraries: 5th European Conference, ECDL 2001, Darmstadt, Germany, September 4-9, 2001. Proceedings

Research and Advanced Technology for Digital Libraries: First European Conference, ECDL '97 Pisa, Italy, September 1-3, 1997 Proceedings

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Research in Cryptology, 2 conf., WEWoRC 2007

Financial Cryptography and Data Security, 11 conf., FC 2007

Advanced Intelligent Computing Theories and Applications, 3 conf., ICIC 2007

Digital Libraries and Archives - IRCDL 2011

Rough Sets and Knowledge Technology, 2 conf., RSKT 2007

Cryptography and Coding, 11 conf

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STACS 2007, 24 conf

Research and Advanced Technology for Digital Libraries, 11 conf., ECDL 2007

Research and Advanced Technology for Digital Libraries: 7th European Conference, ECDL 2003, Trondheim, Norway, August 17-22, 2003. Proceedings

Research and Advanced Technology for Digital Libraries: 12th European Conference, ECDL 2008, Aarhus, Denmark, September 14-19, 2008. Proceedings

Research and Advanced Technology for Digital Libraries: 5th European Conference, ECDL 2001, Darmstadt, Germany, September 4-9, 2001. Proceedings

Research and Advanced Technology for Digital Libraries: First European Conference, ECDL '97 Pisa, Italy, September 1-3, 1997 Proceedings

Research and Advanced Technology for Digital Libraries: 6th European Conference, ECDL 2002, Rome, Italy, September 16-18, 2002, Proceedings

Advanced Language Technologies for Digital Libraries

Digital Libraries

MobileFi: Advanced Research and Technology

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Research and Advanced Technology for Digital Libraries: 14th European Conference, ECDL 2010, Glasgow, UK, September 6-10, 2010, Proceedings (Lecture ... Applications, incl. Internet Web, and HCI)

Research and Advanced Technology for Digital Libraries: 13th European Conference. ECDL 2009, Corfu, Greece, September 27 - October 2, 2009, ... Applications, incl. Internet Web, and HCI)

Research in Computational Molecular Biology, 11 conf., RECOMB 2007

Research and Advanced Technology for Digital Libraries: 8th European Conference, ECDL 2004, Bath, UK, September 12-17, 2004, Proceedings (Lecture Notes in Computer Science)

Digital Human Modeling, 1 conf., ICDHM 2007

Research and Advanced Technology for Digital Libraries: 10th European Conference, EDCL 2006, Alicante Spain, September 17-22, 2006, Proceedings

Semantic Digital Libraries

Understanding Digital Libraries,

Understanding Digital Libraries

Asian Digital Libraries.. Looking Back 10 Years and Forging New Frontiers, 10 conf., ICADL 2007

Persuasive Technology, 2 conf., PERSUASIVE 2007

WiMAX-MobileFi: advanced research and technology

Handbook of Research on Digital Libraries: Design, Development, and Impact

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