Learning query expansion from association rules between terms

Query expansion technique offers an interesting solution for obtaining a complete answer to a user query while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. In this paper, we attempt to use data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. Face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results, we then build a model able to predict which association rules are to be used when expanding a query. The experiments were performed on SDA 95 collection, a data collection for information retrieval. The main observation is that the hybridization of textmining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.