Fuzzy Information Retrieval Model Based on Multiple Related Ontologies

Abstract

With the semantic web progress, encoding of knowledge bases as ontologies has increased. Information retrieval applications are employing this knowledge organization to enhance quality of results by returning documents semantically related and relevant to initial user's query. The proposed fuzzy information retrieval model retrieves information providing a framework to encode a knowledge base composed of multiple related ontologies whose relationships are expressed as fuzzy relations. This knowledge organization is used in a novel method to expand the user initial query and to index the documents in the collection. The model allows the ontologies, as well as the relationships among their concepts, to be represented independently. Experimental results show that the proposed model presents better overall performance when compared with another classical fuzzy-based approach for information retrieval.