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Query expansion using fuzzy association rules between terms

Information retrieval (IR) focuses on the process of determining and assessing the adequacy between a user-query and a collection of documents, yielding a subset of relevant documents. In this respect, query expansion aims to reduce an eventual query/document mismatch by expanding the query using correlated terms. In this paper, we present an approach based on the use of association rules to detect such correlations, in order to improve retrieval effectiveness by reducing such mismatch. By considering the term-document relation as a fuzzy binary relation (with a special emphasis on membership degrees semantic), we propose a fuzzy conceptual approach to extract fuzzy association rules. This approach is based on the closure of an extended fuzzy Galois connection, using different semantics of term membership degrees. An experimental study, on real textual collections, has confirmed our intuitive hypothesis that the synergy between association rules and query expansion is fruitful. Results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision.