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Bayesian Network Models for Information Retrieval

  • Berthier Ribeiro-Neto
  • Ilmério Silva
  • Richard Muntz
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 50)

Abstract

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.

Keywords

Information Retrieval Bayesian Network Information Retrieval System Inference Network Boolean Model 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Berthier Ribeiro-Neto
    • 1
  • Ilmério Silva
    • 1
    • 2
  • Richard Muntz
    • 3
  1. 1.Computer Science DepartmentFederal University of Minas GeraisBelo HorizonteBrazil
  2. 2.Department of InformaticsFederal University of UberlândiaUberlândiaBrazil
  3. 3.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA

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