Towards a belief-revision-based adaptive and context-sensitive information retrieval system

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

52 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Article number8
Journal / PublicationACM Transactions on Information Systems
Issue number2
Publication statusPublished - 1 Mar 2008


In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections. © 2008 ACM.

Research Area(s)

  • Adaptive information retrieval, Belief revision, Information flow, Retrieval context, Text mining