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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.
Fuzzy Set Theory: R.R. Yager: A Framework for Linguistic and Hierarchical Queries in Document Retrieval.- G. Bordogna, G. Pasi: Application of Fuzzy Set Theory to Extend Boolean Information Retrieval.- L. Kóczy, T. Gedeon: A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurence of Words.- J.-W. Lim: Visual Keywords: from Text Retrieval to Multimedia Retrieval.- D. Merkl, A. Rauber: Document Classification with Unsupervised Artificial Neural Networks.- H. Chen, M. Ramsey, P. Li: The Java Search Agent Workshop.- S. Zrehen: A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes.- Genetic Algorithms: M. Boughanem, C. Chrismet, J. Mothe, C. Soule-Dupuy, L. Tamine: Connectionist and Genetic Approaches for Information Retrieval.- D. Vrajizoru: Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval.- Evidential and Probabilistic Reasoning: J. Picard, J. Savoy: A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory.- B. Ribeiro-Neto, I. Silva, R. Muntz: Bayesian Network Models for Information Retrieval.- G. Amati, F. Crestani: Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance.- Rough Sets Theory, Multivalued Logics, and Other Approaches: S.K.M. Wong, Y.Y. Yao, C.J. Butz: Granular Information Retrieval.- U. Straccia: A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Descritpion Logics.- P. Srinivasan, D. Kraft, J. Chen: Rough and Fuzzy Sets for Data Mining of a Controlles Vocabulary for Textual Retrieval.- S. Miyamoto: Rough Sets and Multisets in a Model of Information Retrieval.