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Textual query is the simple mean for communicating with a retrieval system. However, there is a risk of providing an incomplete query which hinders the system from satisfying the user information needs. By reformulating the queries, query expansion is solution for this problem, this mainly relies on an accurate choice of the added terms to an initial query. It can yield a large number of irrelevant terms, which in turn negatively influences quality of retained documents. In this paper, we propose Query Expansion approach. It consists of reformulating queries by semantically related terms extracted from a semantic graph called query graph derived from Wikipedia. Furthermore, we propose a similarity measure which computes the similarity between a candidate terms and initial query using the query graph, Explicit Semantic Analysis (ESA) measure, and text mining technique. The experiments on Text Retrieval Conference (TREC) collection show that the proposed approach performs significantly better than the baseline system and some existing techniques.

Improving Retrieval Performance Based on Query Expansion with Wikipedia and Text Mining Technique