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Information Retrieval (IR) is concerned with the identification of documentsin a collection that are relevant to a given information need, usuallyrepresented as a query containing terms or keywords, which are supposed to be agood description of what the user is looking for. IR systems may improve theireffectiveness (i.e., increasing the number of relevant documents retrieved) byusing a process of query expansion, which automatically adds new terms to theoriginal query posed by an user. In this paper we develop a method of queryexpansion based on Bayesian networks. Using a learning algorithm, we constructa Bayesian network that represents some of the relationships among the termsappearing in a given document collection; this network is then used as athesaurus (specific for that collection). We also report the results obtainedby our method on three standard test collections.

Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus