A fuzzy-ontology-driven method for a personalized query reformulation

Ontologies have proven their utility in the area of Information Retrieval. However, building and updating ontologies manually is a long and tedious task. Moreover, crisp ontologies are not capable to support uncertain information. One interesting solution is to integrate fuzzy logic into ontology to handle vague and imprecise information. This paper presents a method for individual fuzzy ontology building. The key aspects in our proposal are: (1) an automatic building of an individual fuzzy ontology; (2) a query reformulation based, on the one hand, on the weights associated with the concepts and all existing relations in the fuzzy ontology and, on the other hand, on users´ preferences, (3) an update of the membership concepts and relations´ values after each users search, and (4) the use of the proposed fuzzy ontology and service ontology to individually classify documents by services. Our method has endured a twofold evaluation. Firstly, we have evaluated the impact of the update and the weights´ variations on the search results. Secondly, we have studied how the query reformulation has led to a quality results improvement, both in terms of precision and recall.