Ebook: Soft Computing in Information Retrieval: Techniques and Applications

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.

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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.

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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.
Content:
Front Matter....Pages i-xii
Front Matter....Pages 1-1
A Framework for Linguistic and Hierarchical Queries in Document Retrieval....Pages 3-20
Application of Fuzzy Set Theory to Extend Boolean Information Retrieval....Pages 21-47
A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurrence of Words....Pages 48-74
Front Matter....Pages 75-75
Visual Keywords: from Text Retrieval to Multimedia Retrieval....Pages 77-101
Document Classification with Unsupervised Artificial Neural Networks....Pages 102-121
The Java Search Agent Workshop....Pages 122-140
A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes....Pages 141-169
Front Matter....Pages 171-171
Connectionist and Genetic Approaches for Information Retrieval....Pages 173-198
Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval....Pages 199-222
Front Matter....Pages 223-223
A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory....Pages 225-258
Bayesian Network Models for Information Retrieval....Pages 259-291
Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance....Pages 292-313
Front Matter....Pages 315-315
Granular Information Retrieval....Pages 317-331
A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics....Pages 332-357
Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval....Pages 358-372
Rough Sets and Multisets in a Model of Information Retrieval....Pages 373-393
Back Matter....Pages 395-395

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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.
Content:
Front Matter....Pages i-xii
Front Matter....Pages 1-1
A Framework for Linguistic and Hierarchical Queries in Document Retrieval....Pages 3-20
Application of Fuzzy Set Theory to Extend Boolean Information Retrieval....Pages 21-47
A Model of Intelligent Information Retrieval Using Fuzzy Tolerance Relations Based on Hierarchical Co-Occurrence of Words....Pages 48-74
Front Matter....Pages 75-75
Visual Keywords: from Text Retrieval to Multimedia Retrieval....Pages 77-101
Document Classification with Unsupervised Artificial Neural Networks....Pages 102-121
The Java Search Agent Workshop....Pages 122-140
A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes....Pages 141-169
Front Matter....Pages 171-171
Connectionist and Genetic Approaches for Information Retrieval....Pages 173-198
Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval....Pages 199-222
Front Matter....Pages 223-223
A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory....Pages 225-258
Bayesian Network Models for Information Retrieval....Pages 259-291
Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance....Pages 292-313
Front Matter....Pages 315-315
Granular Information Retrieval....Pages 317-331
A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics....Pages 332-357
Rough and Fuzzy Sets for Data Mining of a Controlled Vocabulary for Textual Retrieval....Pages 358-372
Rough Sets and Multisets in a Model of Information Retrieval....Pages 373-393
Back Matter....Pages 395-395
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