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INFORMATION RETRIEVAL: UNCERTAINTY AND LOGICS
THE KLUWER INTERNATIONAL SERIES ON INFORMATION RETRIEVAL
Also in the Series:
Series Editor
w. Bruce Croft
University of Massachusetts Amherst, MA 01003
MULTIMEDIA INFORMATION RETRIEVAL: Content-Based Information Retrieval from Large Text and Audio Databases
by Peter Schauble ISBN: 0-7923-9899-8
INFORMA TION RETRIEVAL SYSTEMS by Gerald Kowalski ISBN: 0-7923-9926-9
CROSS-LANGUAGE INFORMATION RETRIEVAL edited by Gregory Grefenstette ISBN: 0-7923-8122-X
TEXT RETRIEVAL AND FILTERING: Analytic Models of Performance
by Robert M. Losee ISBN: 0-7923-8177-7
INFORMATION RETRIEVAL: UNCERTAINTY AND LOGICS
Advanced Models for the Representation and Retrieval of
Information
edited by
Fabio Crestani Mounia Lalmas
Cornelis Joost van Rijsbergen University of Glasgow
Glasgow, Scotland
~.
" SPRINGER. SCIENCE+BUSINESS MEDIA, LLC
ISBN 978-1-4613-7570-8 ISBN 978-1-4615-5617-6 (eBook) DOI 10.1007/978-1-4615-5617-6
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available from the Library of Congress.
Copyright © 1998 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 1998 Softcover reprint ofthe hardcover 1st edition 1998 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher, Springer Science+Business Media, LLC.
Printed on acid-free paper.
Contents
List of Figures
List of Tables
Preface
Contributing Authors
Part I Genesis
1 A non-classical logic for information retrieval Cornelis Joost van Rijsbergen
1.1 Introduction 1.2 Classical information retrieval 1.3 A conditional logic for information retrieval
1.4 How do we evaluate P(s -t q}?
1.5 Logic of uncertainty 1.6 Conclusion
References
Part II Logical Models
2 Toward a broader logical model for information retrieval Jian- fun Nie and Francois Lepage
2.1 Introduction 2.2 The necessity to consider situational factors 2.3 Toward a model of relevance 2.4 An outline for coping with changes in retrieval situations 2.5 Concluding remarks and further research
References
ix
xi
xiii
xix
3
3 4 8 9
11 12
12
17
17 19 25 31 36
37
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3 Experiences in information retrieval modelling using structured formalisms 39
and modal logic lean-Pierre Chevallet and Yves Chiaramella
3.1 Introduction 39 3.2 Basic hypotheses 40 3.3 A modal retrieval model 46 3.4 A theoretical modal model for information retrieval 3.5 Operational models 3.6 Theoretical logic model and operational graph model 3.7 Conclusion
49 53 66 68
References 70
4 Preferential models of query by navigation Peter Bruza and Bernd van Linder
73
4.1 Introduction 4.2 Information retrieval fundamentals 4.3 Preferential structures, defaults and preclusions 4.4 Sound inference rules for preferential structures 4.5 Related work 4.6 Conclusions and further research
73 77 79 90 93 94
References 95
5 A flexible framework for multimedia information retrieval 97 Adrian Muller
5.1 Introduction 97 5.2 Abductive information retrieval: a framework 100 5.3 Comparing deductive and abductive information retrieval 105 5.4 The abduction procedure for information retrieval: a definition 108 5.5 An application: image retrieval by means of abductive inference 111 5.6 Conclusions 122
References 125
6 The flow of information in information retrieval: towards a general frame- 129
work for the modelling of information retrieval Mounia Lalmas
6.1 Introduction 129 6.2 Situation theory and its connection to information retrieval modelling 131 6.3 Channel theory and its connection to information retrieval modelling 138 6.4 Other frameworks for modelling the flow of information in IR 139 6.5 A general framework for the modelling of information retrieval 141 6.6 Application of the model 145 6.7 Conclusion 148
References
7 Mirlog: a logic for multimedia information retrieval Carlo Meghini, Fabrizio Sebastiani and Umberto Straccia
7.1 Introduction 7.2 Syntax and classical semantics 7.3 A relevance semantics 7.4 Closures 7.5 Modelling uncertainty 7.6 Reasoning in MIRLOG
7.7 Conclusions
References
Part III Uncertainty Models
8 Semantic information retrieval Gianni Amati and Keith van Rijsbergen
8.1 Introduction to semantic information theory 8.2 An overview from the information retrieval perspective 8.3 The notion of information content 8.4 Entropy and information content 8.5 Duality theory 8.6 Conclusions
References
9 Information retrieval with probabilistic Datalog Thomas Rill/eke and Norbert Fuhr
9.1 Introduction 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 9.11 9.12 9.13
Sample document retrieval Hypertext structure Logical structure Class hierarchy Terminological knowledge Object-oriented knowledge representation Retrieval and uncertain inference Syntax of Datalogp Semantics Evaluation of probabilistic Datalog programs Independence and disjointness assumptions Conclusion and outlook
References
Contents vii
148
151
151 154 157 165 173 179 180
182
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189 190 194 196 211 216
217
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221 223 223 224 225 225 227 230 234 236 240 242 243
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10 Logical imaging and probabilistic information retrieval Fabio Crestani
10.1 Introduction 10.2 Relevance as logical implication 10.3 The probabilistic retrieval space 10.4 Imaging and information retrieval 10.5 Imaging and word senses 10.6 Implementation issues 10.7 Experimentation issues 10.8 Related work 10.9 Conclusions
References
11 Simplicity and information retrieval Gianni Amati and Keith van Rijsbergen
11.1 Introduction 11.2 Simplicity and the shortest descriptions 11.3 Comparison with the minimum description length principle 11.4 Conclusions
References
Part IV Meta-Models
12 Towards an axiomatic aboutness theory for information retrieval Theo Huibers and Bernd Wondergem
12.1 Introduction 12.2 The evaluation of information retrieval systems 12.3 Situation theory 12.4 Framework for meta-evaluation of IR systems 12.5 Investigation of aboutness 12.6 Examples 12.7 Conclusion and further research
References
247
247 248 252 258 263 265 272 273 275
275
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281 282 290 292
293
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297 298 301 302 307 309 315
316
List of Figures
1.1 P( "" A) large, P(B I A) -+ o. 9 2.1 A view of computational models and cognitive models. 18 2.2 An example of spheres of worlds. 29 2.3 Examples of evaluation of counterfactual conditionals. 30 3.1 Matching criteria. 42 3.2 Example of concept: "opacity of the lung". 54 3.3 A simple taxonomy. 61 3.4 An example of projection. 63 4.1 Before refining. 81 4.2 After refining. 82 4.3 Refinement structures. 83 4.4 General refinement structure. 84 4.5 Hyperindex fragment. 87 5.1 General Structure of an SPSS Box-Plot. 115 5.2 Two box-plots for contour (of front object) for feature entropy. 115 5.3 Graphical patterns for rule synthesis. 116 5.4 (Subset of) rule-base for texture and colour based retrieval. 119 5.5 Query Q initial: A reference image vroom3 with classification infor-
mation. 120 5.6 Query Q1: Images with sharp contour and artificial objects. 121 5.7 Query Q2: Colour composition like vroom3. 123 5.8 Query Q3: Restricting colour composition by intersection of colour
tables (for high selectivity). 124 7.1 A MIRLOG knowledge base. 162 7.2 Another MIRLOG knowledge base. 166 7.3 Tableaux for a = (A V B) /\ ("" A V B) /\ ("" A V "" B). 180 9.1 Sample document retrieval. 223 9.2 Hypertext structure. 224 9.3 Object-oriented modelling. 228
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9.4 Object-oriented representation of documents. 9.5 Syntax of probabilistic Datalog. 10.1 The classical semantics for the term space. 10.2 Application of the PWS to the term space. 12.1 The experimental paradigm.
229 236 257 257 298
List of Tables
8.1 8.2 8.3 8.4 8.5 8.6 10.1 10.2 10.3 10.4
10.5 10.6 10.7
Contingency tables. 205 The expected information of sentences in logic programs. 208 The expected utility of sentences in logic programs. 208 Other contingency tables. 209 Examples of ran kings. 210 A summary of the duality theory. 215 Example of the evaluation of P(d -+ q) by imaging on d. 259 Example of the evaluation of P(d -+ q) by general imaging on d. 260 Example of the evaluation of P(d -+ q) by proportional imaging on d. 261 Example of the evaluation of P(d -+ q) by mixed general imaging oo~ ~2
Evaluation of P(d2 -+ q) by imaging on d2 . 264 Evaluation of P (d l -+ q) by imaging on d l . 264 Evaluation of P(q -+ dd by imaging on q. 265
11.1 Some models of information content. 288 11.2 Examples of contingency tables: MDL returns equal values for TI
and T2 • 291
Xl
Preface
Information retrieval (IR) is the science and technology concerned with the effective and efficient retrieval (and storage) of information for the subsequent use by interested parties. The central problem in IR is the quest to find the set of relevant documents, amongst a large collection, containing the information sought thereby satisfying an information need usually expressed by a user with a query. The documents may be objects or items in any medium, text, image, audio, or, indeed a mixture of all three. An important area of research concentrates on the modelling of such objects and processes involved in the retrieval of information.
Well known models in IR are the Boolean, vector space, probabilistic, and fuzzy models; these have been studied in detail and implemented for experimentation and in some cases for commercial purposes. Nevertheless, the known limitations of these models have caused researchers to propose new models from time to time. One such model is the logical model for IR.
In recent years there have been several attempts to define a logic for IR. Logical IR models were studied to provide a rich and uniform representation of information and its semantics with the aim to improve retrieval effectiveness. The earliest approaches were directed to the use of classical logic, like Boolean logic. The basis of a logical model for IR is the assumption that queries and documents can be represented effectively by logical formulas. In order to retrieve a document, an IR system has to infer the formula representing the query from formulas representing the document. This logical interpretation of query and documents emphasises that information retrieval is an inference process that computes whether a document d is relevant to a query q using both information present in the document itself and user knowledge. A simple example is given in classical logic where inference is often associated with logical implication: a document is relevant to a query if it implies the query, or in other words, if the query can be inferred from the document. Such an evaluation formally embodies the semantics of the information represented in the query and in the document.
The use of logic to build IR models enables one to obtain models that are more general than earlier well known IR models. Indeed, some logical models are able to represent within a uniform framework various features of IR systems, such as hypermedia links, multimedia content, and users knowledge. It also provides a common
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approach to the integration of IR systems with logical database systems. Finally, logic makes it possible to reason about an IR model and its properties. This latter possibility is becoming increasingly important since conventional evaluation methods, although good indicators of the effectiveness of IR systems, often give results which cannot be predicted, or for that matter satisfactorily explained.
However, logic by itself cannot fully model IR. In determining the relevance of a document to a query the success or failure of an implication relating the two to go through is not enough. It is necessary to take into account the uncertainty inherent in such an implication. To cope with uncertainty a logic for probabilistic inference was introduced. If d -t q is uncertain, then we can measure its degree of uncertainty by P(d -t q). In 1986 Van Rijsbergen proposed the use of a non-classical conditional logic for IR (see chapter 1). This would enable the evaluation of P(d -t q) using the following logical uncertainty principle:
"Given any two sentences x and y; a measure of the uncertainty of y -t x related to a given data set is determined by the minimal extent to which we have to add information to the data set, to establish the truth of y -t x."
This principle was the one of first attempts to make an explicit connection between non-classical logics and IR modelling. However, when proposing the above principle, Van Rijsbergen was not specific about which logic and which uncertainty theory to use. As a consequence, various logics and uncertainty theories have been proposed and investigated. The choice of the appropriate logic and uncertainty mechanisms has been a main research theme in logical IR modelling leading to a number of different approaches over the years.
This book contains a collection of exciting papers proposing, developing and implementing logical IR models. We have classified the different approaches into three groups:
• Logical Models: these are models mainly based on a logic. The uncertainty is captured in two ways: qualitatively by the logic itself (for example, via default rules, non-monotonicity, or background conditions), or quantitatively by adding an uncertainty theory to the logic (for example, fuzzy logic).
• Uncertainty Models: these are models mainly based on an uncertainty theory (for instance, probability theory, semantic theory, imaging) that is defined on a logical basis.
• Meta-models: these are models proposed as a logical framework in which IR systems can be represented so that their properties and their effectiveness can be formally studied, and in some cases proved.
Some of the most important instances of these groups of models are presented in this book. The book is divided in four parts, I to IV, where each part other than the first corresponds to one of the groups of models discussed above.
Part I, entitled Genesis has only one chapter, chapter 1, which is a reprinted version of the 1986 seminal paper by Keith van Rijsbergen. We decided to reprint that paper for its importance in relation to the topics addressed in this book and because of the
PREFACE xv
direct influence it had on the work presented. The chapter shows how a non-classical logic is the appropriate formalism with which to build IR models. It also investigates the relationship with existing retrieval mechanisms, and for the first time a "logical uncertainty principle" is defined introducing a measure of the uncertainty associated with the inference d --+ q.
Part II, entitled Logical Models of Information Retrieval, contains 6 chapters, numbered 2 to 7. In chapter 2, Jean-Yun Nie and Franrois Lepage suggest a framework in which topicality is not the only query satisfaction criterion. Factors such as users' beliefs and information states are considered and encompassed in a retrieval situation represented as a belief set. The satisfaction between a document and a query is represented as a counterfactual conditional d --+ q whose evaluation depends on the given retrieval situation. This framework can be viewed as a step towards incorporating cognitive aspects into an IR model. In chapter 3, Jean-Pierre Chevallet and Yves Chiaramella present major developments of a retrieval model in terms of a fuzzy modal logic with applications to two formalisms: semantic dependencies and conceptual graphs. Their aim is an integration between fuzzy logic and knowledge representation in the context of IR. Their chapter shows that conceptual graphs have formal properties that allow control of this integration in a way that is well adapted to IR requirements. This has triggered extensions of their initial logical model with adaptations to the conceptual graph formalism for modelling IR. In chapter 4, Peter Bruza and Bernd van Linder present a framework that can be seen as integrating non-monotonic reasoning with IR. The authors consider the searching process as navigation through an information space, where user preferences suggested by the path are represented as defaults and/or preclusion relationships. They define the semantics of navigation paths in the style of model preference logic giving some IR related properties. Sound inference rules for this semantics are also provided, and can be used for query expansion or for dynamically altering the information space through which the user is browsing. In chapter 5, Adrian Muller uses abductive inference, a non-monotonic reasoning mechanism which operates in a task-driven way, to build a model for multimedia and hypermedia retrieval. He presents a procedural definition of an appropriate semantics for abduction in the field of IR. An example in terms of image retrieval is sketched where the abductive framework is applied to work with imprecise rules, describing the computable properties of images. In chapter 6, Mounia Lalmas proposes a general IR framework based on the notion of the flow of information which characterises information containment. The modelling approach is based on channel theory (a development arising out of Situation Theory), a theory that formalises properties attributed to information and its flow between situations. She shows the connection between channel theory and IR, and how the framework can encompass various features that are becoming more predominant in hypertext systems, user modelling, and query expansion. In chapter 7, Carlo Meghini, Fabrizio Sebastiani and Umberto Straccia present a logic for the retrieval of multimedia information. The logic presented is the product of a
xvi INFORMATION RETRIEVAL: UNCERTAINTY AND LOGICS
number of extensions to a description logic, which constitutes the kernel of the model. Each extension is meant to capture an important aspect of the retrieval endeavour that is not adequately dealt with by the kernel logic. The resulting logic is to be understood as a modelling retrieval tool, which can be used for the specification and the rapid prototyping of applications.
Part III describes Uncertainty Models for Information Retrieval and contains 4 chapters, numbered 8 to 11. Gianni Amati and Keith van Rijsbergen introduce, in chapter 8, semantic information theory for logical sentences based on different notions of information content. An axiomatisation of conditional information content is given. They investigate the connection between information content and probability. They show that probability is not the only basis for giving a quantitative definition of amount of information, and that concepts, like simplicity, regularity, randomness, and shortest description length, formalise different notions of the information content of sentences. These ideas are then applied showing that the Robertson and Sparck Jones weighting formula is suboptimal from a certain point of view. They conclude by presenting a Duality Theory that can be used as a tool to compare different IR models. In chapter 9, Thomas Rolleke and Norbert Fuhr describe an approach to the computation of the probability of d ---+ q. Their work provides a platform for the investigation of the probabilistic logical models in IR. It allows for the modelling of the content of documents, the logical structure of documents and the relationships between information objects. Their results can be applied to hypermedia applications, and can be integrated with the logical approach of databases. In chapter 10, Fabio Crestani specifies an IR framework based on logical imaging. Logical imaging is a non-standard probability revision technique originally proposed in the area of conditional logic that enables the evaluation of a conditional sentence without explicitly defining the semantics of the conditional operator. This chapter presents a new class of models of probabilistic IR. All models belonging to this class are based on a new kinematics of probabilities in the probabilistic term space that takes into account semantic similarity between terms. This kinematics is very different from the one induced by classical models of IR and exploit in a better way the information present in the term space. Different models are derived from different forms of logical imaging. This contribution is to be seen as describing a new theoretical framework for investigating probabilistic IR. In chapter 11, Gianni Amati and Keith van Rijsbergen study concepts like simplicity, regularity, randomness, shortest description length in formalising the information content of documents. They show that a form of Zipf's law and the inverse document frequency weight can be derived from principles involving these concepts.
Part IV describes Meta-models for Information Retrieval, and contains one chapter, chapter 12. There, Thea Huibers and Bernd Wondergen explicate a meta-theory for studying information retrieval. The authors introduce the concept of aboutness, a version of which underlies every IR model: a document satisfies a query if it is about that query. The meta-theory described in this chapter offers the possibility of axiomatising the aboutness relation for every IR model. Based on this, information
PREFACE xvii
retrieval systems can be analysed and compared in a way that goes beyond empirical methods.
The idea of producing this book originated in July 1996 when many of the contributors to this book met in Glasgow at the Second Workshop on Information Retrieval, Uncertainty and Logics. We realised then how important logical IR modelling had become, and how many researchers around the world were interested in its ideas and ramifications. David Blair once commented to one of us: "I have become somewhat concerned that there aren't any obvious IR people to continue the foundational work of Cooper, Maron, et al. . .. Do you think there is anything we could do to promote the investigation of foundational issues in IR?" There is! And this is it. We hope this book will attract even more interest to this exciting area of research in IR.
Given the complexity of some of the topics addressed, this book is aimed mainly at advanced undergraduate and postgraduate students with some background in IR. Apart from the obvious contribution to IR research, the book can be seen to be of interest to those working in information systems, logics and theories of uncertainty. It may also be useful to software designers and developers who are interested in knowing more about the models and tools to support advanced information representation and searching.
Acknowledgements
There are many people and institutions that we would like to thank for the help provided and the financial support that was necessary to the preparation of this book. We apologise if we will not mention them all.
First of all we would like to thank all the authors of the various chapters for inter-refereeing each other manuscripts, which helped us to secure the quality of the technical content of the book. Next we would like to express our special gratitude to Juliet van Rijsbergen for tirelessly improving the readability of the book, of course all the remaining errors and infelicities in the text remain the responsibility of the editors and authors. In addition, we would like to thank all the people (family and friends) that helped and supported us during the long hours of work it took to complete the book.
Finally, on the financial side, we would like to acknowledge the support of Universities of Padova, Dortmund and, mostly, Glasgow. A good part of the work reported in this book has been developed during the Esprit "FERMI" Project, that also supported the participation of many of the contributors to the Second Workshop on Information Retrieval, Uncertainty and Logics held in Glasgow in July 1996.
FABIO CRESTANI, MOUNIA LALMAS, KEITH VAN RrrSBERGEN
Contributing Authors
Giambattista Amati is researcher at the "U go Bordoni" Foundation in Rome in the group of "Information Systems" of the Multimedia Communications Division. His current research concerns the investigation, development and application of logical and probabilistic theories of reasoning and decision-making under uncertainty.
Peter Bruza is a lecturer at the School of Information Systems, Queensland University of Technology. From 1992 to 1993, he was lecturer at Utrecht University, The Netherlands. He holds an MSc and PhD in Computer Science from the University of Nijmegen, The Netherlands. His main interest is applying logic to information retrieval.
Jean-Pierre Chevallet is currently maitre de conferences at the Universite Pierre Mendes France de Grenoble. He holds a PhD in Computing Science from the Universite Joseph Fourier of Grenoble.
Yves Chiaramella is professor in Computer Science at the Universite Joseph Fourier, Grenoble. He is Head of CLIPS-I MAG, a Computer Science Laboratory dedicated to Man-Machine Communication. He has been involved in information retrieval research for 15 years, and currently manages a group on multimedia information retrieval within the CLIPS Laboratory. His interests in the field are models of indexing for complex, structured objects such as multimedia documents, and logic-based information retrieval models.
Fabio Crestani is a "Marie Curie" research fellow at the Department of Computing Science of the University of Glasgow. From 1992 to 1997, he was assistant professor at the University of Padua, Italy. He holds a degree in Statistics from the University of Padua, and an MSc and PhD in Computing Science from the University of Glasgow. He is interested in logical and probabilistic modelling of multimedia information retrieval and hypermedia.
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Norbert Fuhr is professor in the Computer Science Department of the University of Dortmund, Germany since 1991. He holds a PhD in Computer Science from the Technical University of Darmstadt, Germany. His current research focuses on logic-based information retrieval, multimedia retrieval and digital libraries.
Theo Huibers is manager at DOXi S, a Dutch company that focuses on all aspects of document and information management. From 1996 to 1997, he held a postdoctoral position at the University of Nijmegen. He holds an MSc in Computer Science from the University of Nijmegen and a PhD in Computer Science from the University of Utrecht.
Mounia Lalmas is a part-time research fellow at the Department of Computing Science at the University of Glasgow. She is also a part-time research assistant at Informatik VI, University of Dortmund. From 1995 to 1997, she was a lecturer at the Department of Computer Science, at Glasgow University. She holds an MAppSc and a PhD in Computing Science from the University of Glasgow.
Fran~ois Lepage is a professor and the director of the Department of Philosophy of the Universite de Montreal. He holds a PhD on logics from the Universite Paris V.
Carlo Meghini has been staff researcher at the Istituto di Elaborazionedell'Informazione, Consiglio Nazionale delle Ricerche, in Pisa, Italy, since 1984. He holds a degree in Computer Science from the University of Pisa.
Adrian Mueller is working for IBM Germany, Software Solutions Development (SWSD), in the field of information retrieval, text mining and related fields. From 1992 to 1997, he was a member of the department MIND (Multimedia Information retrieval Dialogue techniques), which is a research group at GMD-IPSI (Integrated Publication and Information Systems Institute).
Jian-Yun Nie is an associate professor at the Department of Computer Science and Operation Research of the Universite de Montreal. He holds a PhD in Computer Science from the Universite Joseph Fourier in Grenoble, France.
Thomas Rolleke has worked at the Department of Computing Science of the University of Dortmund since 1994. His research topics are probabilistic data models, objectoriented modelling, logic, and hypermedia information retrieval. He holds a diploma in Engineering Computing Science of the University of Dortmund and was Unix Marketing Consultant at the Nixdorf Computer AG.
CONTRIBUTING AUTHORS xxi
Fabrizio Sebastiani is staff researcher at the Istituto di Elaborazione dell'Informazione, Consiglio Nazionale delle Ricerche, in Pisa, Italy, since 1988. Between 1986 and 1988 he was a research fellow at the Department of Linguistics, University of Pisa. He received a degree in Computer Science from the University of Pisa in 1986. In the recent years he has lectured on logical foundations of artificial intelligence, databases and information systems, and information retrieval, at the Universities of Pis a, Perugia, and L' Aquila, Italy.
Umberto Straccia is currently a research associate at the Istituto di Elaborazione dell'Informazione, Consiglio Nazionale delle Ricerche, in Pisa, Italy. He holds a degree in Computer Science from the University of Pisa, Italy, and is a PhD student in Computing Science at the University of Dortmund, Germany.
Bernd van Linder is working in industry as systems analyst for the ABN-AMRO bank, The Netherlands. From 1995 to 1997, he was a research scientist at the Phillips National Research Laboratories, The Netherlands. He holds an MSc in Computer Science from the University of Nijmegen and a PhD in Computer Science from Utrecht University.
Cornelis Joost van Rijsbergen is professor of Computing Science at the University of Glasgow. He has been active in information retrieval research since 1968. He is the author of "Information Retrieval" a well known book in the field. His current research is concentrated on dimensionality reduction, clustering, and logic-based information retrieval.
Bernd Wondergem is a PhD student at the University of Nijmegen. He holds an MSc in Computing Science from the University of Utrecht. He is working for the PROFILE project, aiming at a proactive information filter.
