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E-book: Soft Computing in Information Retrieval: Techniques and Applications

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Information retrieval (IR) aims at defining systems able to provide a fast and effective content-based access to a large amount of stored information. The aim of an IR system is to estimate the relevance of documents to users' information needs, expressed by means of a query. This is a very difficult and complex task, since it is pervaded with imprecision and uncertainty. Most of the existing IR systems offer a very simple model of IR, which privileges efficiency at the expense of effectiveness. A promising direction to increase the effectiveness of IR is to model the concept of "partially intrinsic" in the IR process and to make the systems adaptive, i.e. able to "learn" the user's concept of relevance. To this aim, the application of soft computing techniques can be of help to obtain greater flexibility in IR systems.
Foreword v Preface vii Part I. Fuzzy Set Theory A Framework for Linguistic and Hierarchical Queries in Document Retrieval 3(18) Ronald R. Yager Application of Fuzzy Set Theory to Extend Boolean Information Retrieval 21(27) Gloria Bordogna Gabriella Pasi A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurrence of Words 48(29) Laszlo Koczy Tamas Gedeon Part II. Neural Networks Visual Keywords: from Text Retrieval to Multimedia Retrieval 77(25) Joo-Hwee Lim Document Classification with Unsupervised Artificial Neural Networks 102(20) Dieter Merkl Andreas Rauber The Java Search Agent Workshop 122(19) Hsinchun Chen Marshall Ramsey Po Li A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes 141(32) Stephane Zrehen Part III. Genetic Algorithms Connectionist and Genetic Approaches for Information Retrieval 173(26) Mohand Boughanem Claude Chrisment Josiane Mothe Chantal Soule-Dupuy Lynda Tamine Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval 199(26) Dana Vrajitoru Part IV. Evidential and Probabilistic Reasoning A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory 225(34) Justin Picard Jacques Savoy Bayesian Network Models for Information Retrieval 259(33) Berthier Ribeiro-Neto Ilmerio Silva Richard Muntz Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance 292(25) Gianni Amati Fabio Crestani Part V. Rough Sets Theory, Multivalued Logics, and Other Approaches Granular Information Retrieval 317(15) S.K. Michael Wong Y.Y. Yao Cory J. Butz A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics 332(26) Umberto Straccia Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval 358(15) Padmini Srinivasan Donald Kraft Jianhua Chen Rough Sets and Multisets in a Model of Information Retrieval 373 Sadaaki Miyamoto
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