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Return to Session IVa: Formal models - 2
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.
@inproceedings{1009017, author = {Raymond Y.K. Lau and Peter D. Bruza and Dawei Song}, title = {Belief revision for adaptive information retrieval}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {130--137}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009017}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
