Matching relevance features with ontological concepts

Shen, Yan and Li, Yuefeng and Xu, Yue and Tao, Xiaohui (2012) Matching relevance features with ontological concepts. In: 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WPRS 2012), 4-7 Dec 2012, Macau, China.

Abstract

In order to comprehend user information needs by concepts, this paper introduces a novel method to match relevance features with ontological concepts. The method first discovers relevance features from user local instances. Then, a concept matching approach is developed for matching these features to accurate concepts in a global knowledge base. This approach is significant for the transition of informative descriptor and conceptional descriptor. The proposed method is elaborately evaluated by comparing against three information gathering baseline models. The experimental results shows the matching approach is successful and achieves a series of remarkable improvements on search effectiveness.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2012 IEEE. 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: 17 Apr 2013 02:04
Last Modified: 25 Aug 2016 05:20
Uncontrolled Keywords: relevance feature; concept matching; local instance; global knowledge base
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080602 Computer-Human Interaction
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/WI-IAT.2012.194
URI: http://eprints.usq.edu.au/id/eprint/23119

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