Belief revision for adaptive information retrieval

Raymond Y.K. Lau*, Peter D. Bruza, Dawei Song

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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 IR system 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.

Original languageEnglish
Title of host publicationProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery (ACM)
Pages130-137
Number of pages8
ISBN (Print)1581138814, 9781581138818
DOIs
Publication statusPublished - 2004
Externally publishedYes
EventProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Sheffield, United Kingdom
Duration: 25 Jul 200429 Jul 2004

Publication series

NameProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

ConferenceProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Country/TerritoryUnited Kingdom
CitySheffield
Period25/07/0429/07/04

Keywords

  • Belief Revision
  • IR Context
  • Logic-Based IR

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Lau, R. Y. K., Bruza, P. D., & Song, D. (2004). Belief revision for adaptive information retrieval. In Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 130-137). (Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery (ACM). https://doi.org/10.1145/1008992.1009017