  Information Retrieval (IR) is concerned with the identification of documents
in a collection that are relevant to a given information need, usually
represented as a query containing terms or keywords, which are supposed to be a
good description of what the user is looking for. IR systems may improve their
effectiveness (i.e., increasing the number of relevant documents retrieved) by
using a process of query expansion, which automatically adds new terms to the
original query posed by an user. In this paper we develop a method of query
expansion based on Bayesian networks. Using a learning algorithm, we construct
a Bayesian network that represents some of the relationships among the terms
appearing in a given document collection; this network is then used as a
thesaurus (specific for that collection). We also report the results obtained
by our method on three standard test collections.
