Applying cross-topic relationships to incremental relevance feedback

Terry C.H. Lai, Stephen C.F. Chan, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

General purpose search engines like Google and Yahoo define search topics for the purpose of document organization, yet their hierarchical structures cover only a portion of topic relationships. Search effectiveness can be improved by using search topic networks, in which topics are linked through semantic relations. In our search model, is-child and is-neighbor relations are defined as relations among search topics, which in turn can serve as search techniques; the is-child relation allows searching from general concepts, while the is-neighbor relation provides fresh information that can help users to identify search areas. This search model uses the Bayesian Networks and the incremental relevance feedback. Our experiments show that search models using the Bayesian Networks and the incremental relevance feedback improve search effectiveness.
Original languageEnglish
Title of host publicationICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems
Pages356-363
Number of pages8
Publication statusPublished - 23 Nov 2004
EventICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems - Porto, Portugal
Duration: 14 Apr 200417 Apr 2004

Conference

ConferenceICEIS 2004 - Proceedings of the Sixth International Conference on Enterprise Information Systems
CountryPortugal
CityPorto
Period14/04/0417/04/04

Keywords

  • Bayesian Networks
  • Internet Information Retrieval
  • Search Topics

ASJC Scopus subject areas

  • Engineering(all)

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