Ontology mining for semantic interpretation of user information needs

Tao, Xiaohui and Li, Yuefeng and Nayak, Richi (2007) Ontology mining for semantic interpretation of user information needs. In: 2nd International Conference on Knowledge Science, Engineering, and Management (KSEM 2007) , 28-30 Nov 2007, Melbourne, Australia.


Ontology is an important technique for semantic interpre- tation. However, the most existing ontologies are simple computational models based on only 'super-' and 'sub-class' relationships. In this paper, a computational model is presented for ontology mining, which analyzes the semantic relations of 'part-of', 'kind-of' and 'related-to', and interprets the semantics of individual information need. The model is evaluated by comparing the knowledge mined by it, against the knowledge generated manually by linguists. The proposed model enhances Web information gathering from keyword-based to subject(concept)-based. It is a new contribution to knowledge engineering and management.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher. Series: Lecture Notes in Computer Science vol 4798.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 25 Aug 2016 05:18
Last Modified: 25 Aug 2016 05:18
Fields of Research : 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
20 Language, Communication and Culture > 2004 Linguistics > 200408 Linguistic Structures (incl. Grammar, Phonology, Lexicon, Semantics)
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-3-540-76719-0_32
URI: http://eprints.usq.edu.au/id/eprint/20191

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