NDLI: Belief revision for adaptive information retrieval

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Belief revision for adaptive information retrieval

Content Provider	ACM Digital Library
Author	Bruza, Peter D. Song, Dawei Lau, Raymond Y.K.
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.
Starting Page	130
Ending Page	137
Page Count	8
File Format	PDF
ISBN	1581138814
DOI	10.1145/1008992.1009017
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-07-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Logic-based ir Ir context Belief revision
Content Type	Text
Resource Type	Article

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