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

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

Using a Belief Revision Operator for Document Ranking in Extended Boolean Models

This paper claims that Belief Revision can be seen as a theoretical framework for document ranking in Extended Boolean Models. For a model of Information Retrieval based on propositional logic, we propose a similarity measure which is equivalent to a P-Norm case. Therefore it shares the PNorm good properties and behaviour. Besides, it is theoretically ensured that this measure follows the notion of proximity between the documents and the query. The logical model can naturally deal with incomplete descriptions of documents and the similarity values are also obtained for this case. 1 Introduction Logical approaches have been proposed to model Information Retrieval (IR) in a formal framework. Van Rijsbergen was the pioneer in thinking that logic could help in the retrieval of relevant documents [21]. Moreover, he proposed logic as a new theoretical framework for investigating IR. Given d, a logical representation of a document, and q, a logical representation of a query, retrieval is si..

Using a Belief Revision Operator for Document Ranking in Extended Boolean Models

This paper claims that Belief Revision can be seen as a theoretical framework for document ranking in Extended Boolean Models. For a model of Information Retrieval based on propositional logic, we propose a similarity measure which is equivalent to a P-Norm case. Therefore it shares the P-Norm good properties and behaviour. Besides, it is theoretically ensured that this measure follows the notion of proximity between the documents and the query. The logical model can naturally deal with incomplete descriptions of documents and the similarity values are also obtained for this case.