Ontology mining for personalized search

Li, Yuefeng and Tao, Xiaohui (2009) Ontology mining for personalized search. In: Data mining for business applications. Springer, New York, NY. United States, pp. 63-78. ISBN 978-0-387-79419-8

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Abstract

Knowledge discovery for user information needs in user local information repositories is a challenging task. Traditional data mining techniques cannot provide a satisfactory solution for this challenge, because there exists a lot of uncertainties in the local information repositories. In this chapter, we introduce ontology mining,
a new methodology, for solving this challenging issue, which aims to discover interesting and useful knowledge in databases in order to meet the specified constraints on an ontology. In this way, users can efficiently specify their information needs on the ontology rather than dig useful knowledge from the huge amount of discorded patterns or rules. The proposed ontology mining model is evaluated by applying to an information gathering system, and the results are promising.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2009 Springer US. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 19 Nov 2014 04:42
Last Modified: 19 Nov 2014 04:42
Uncontrolled Keywords: knowledge-based information gathering; local instance repository; ontology; user background knowledge; user information needs; world knowledge base
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080603 Conceptual Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-0-387-79420-4_5
URI: http://eprints.usq.edu.au/id/eprint/20195

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