Fuzzy information retrieval based on multi-relationship fuzzy concept networks

Abstract

In this paper, we present a new method for fuzzy information retrieval based on multi-relationship fuzzy concept networks. There are four kinds of fuzzy relationships in a multi-relationship fuzzy concept network, i.e., "fuzzy positive association" relationship, "fuzzy negative association" relationship, "fuzzy generalization" relationship and "fuzzy specialization" relationship. By performing fuzzy inferences based on the multi-relationship fuzzy concept network, the fuzzy information retrieval system can retrieve documents containing concepts that are not directly specified by the user but are somehow related to the user's query. In order to perform fuzzy inferences more efficiently, we use concept matrices to represent the degrees of fuzzy relationships between concepts in a multi-relationship fuzzy concept network. By calculating the transitive closures of concept matrices, the implicit degrees of fuzzy relationships between concepts are obtained. Multiple degrees of satisfaction that a document satisfies the user's query with respect to the fuzzy relationships between concepts are calculated. These satisfaction degrees are aggregated according to the user's specification to find the most relevant documents with respect to the user's query. The proposed fuzzy information retrieval method is more flexible and more intelligent than the one we presented in (IEEE Trans. Systems Man Cybernet.-Part B: Cybernet. 29(1) (1999) 126). (C) 2002 Elsevier B.V. All rights reserved.