Bayesian Network Models for Information Retrieval
Berthier A. Ribeiro-Neto, Ilmério Silva, Richard R. Muntz · Studies in fuzziness and soft computing · 2000
In this chapter, we apply Bayesian networks to the problem of retrieving information about a subject or topic and show that Bayesian networks provide an effective and flexible framework for dealing with information retrieval (IR) in general. Our discussion focus on two Bayesian networks models proposed in the literature namely, the inference network and the belief network models. We compare the expressiveness of these two models and show that the belief network model is more general. We also demonstrate that the belief network model is general enough to subsume the three classic IR models namely, the Boolean, the vector, and the probabilistic models. Further, we show that a belief network can be used to naturally incorporate pieces of evidence from past user sessions which leads to improved retrieval Performance. At the end, for comparative purposes, we review models of reasoning other than the Bayesian networks and characterize a taxonomy for them. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.