A Fuzzy Information Retrieval Method Based on Multi-Relationship Fuzzy Concept Networks

Abstract

This paper presents a fuzzy information retrieval method based on multi-relationship fuzzy concept networks. There are six kinds of fuzzy relationships in a multi-relationship fuzzy concept network, i.e., “fuzzy positive association” relationship, “fuzzy negative association” relationship, “fuzzy kind of” relationship, “fuzzy instance of” relationship, “fuzzy superclass to” relationship, and “fuzzy classify” relationship. We use relevance matrices to model multi-relationship fuzzy concept networks. By calculating the transitive closure of relevance matrices, the implicit relevance degrees between concepts can be obtained. The satisfaction degrees that a document satisfies the user’s queries are then calculated when the concepts contained in a document and the concepts in the user’s query are related by different relationships. The users of fuzzy information retrieval systems could set different importance weights to these multi-relationship satisfaction degrees according to their needed information. The fuzzy information retrieval systems then aggregate these multi-relationship satisfaction degrees to find the most relevant documents with respect to the users’ queries. The proposed fuzzy information retrieval method can be more flexible than the existing methods