Context sensitive text mining and belief revision for adaptive information retrieval

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

7 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - IEEE/WIC International Conference on Web Intelligence, WI 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-262
ISBN (Print)0769519326, 9780769519326
Publication statusPublished - 2003
Externally publishedYes

Conference

TitleIEEE/WIC International Conference on Web Intelligence, WI 2003
PlaceCanada
CityHalifax
Period13 - 17 October 2003

Abstract

Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user's information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via context sensitive text mining. We illustrate a novel approach of integrating the proposed text mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power.

Research Area(s)

  • Australia, Databases, Electronic mail, Information retrieval, Information technology, Internet, Output feedback, Search engines, Technological innovation, Text mining

Citation Format(s)

Context sensitive text mining and belief revision for adaptive information retrieval. / Lau, R. Y K.
Proceedings - IEEE/WIC International Conference on Web Intelligence, WI 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 256-262 1241202.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review