Abstract
We review the application of Bayesian belief networks to several information retrieval problems, showing that they provide an effective and flexible framework for modeling distinct sources of evidence in support of a ranking. To illustrate, we explain how Bayesian networks can be used to represent the classic vector space model and demonstrate how this basic representation can be extended to naturally incorporate new evidence from distinct information sources. These models have been shown useful in several text collections, where the combination of evidential information derived from past queries, thesauri, and the link structure of Web pages has led to significant improvements in retrieval performance.
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