Belief revision for adaptive information retrieval

  • Authors:

  • Raymond Y.K. Lau;Peter D. Bruza;Dawei Song

  • Affiliations:

  • Queensland University of Technology, Brisbane, Australia;The University of Queensland, Brisbane, Australia;The University of Queensland, Brisbane, Australia

  • Venue:
  • Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2004

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Abstract

Applying Belief Revision logic to model adaptive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a retrieval context can be formalised as a belief set and the formalised context is used to disambiguate vague user queries. Belief revision logic also provides a robust computational mechanism to revise an IR system's beliefs about the users' changing information needs. In addition, information flow is proposed as a text mining method to automatically acquire the initial IR contexts. The advantage of a belief-based IRsystem is that its IR behaviour is more predictable and explanatory. However, computational efficiency is often a concern when the belief revision formalisms are applied to large real-life applications. This paper describes our belief-based adaptive IR system which is underpinned by an efficient belief revision mechanism. Our initial experiments show that the belief-based symbolic IR model is more effective than a classical quantitative IR model. To our best knowledge, this is the first successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.