Adopting relevance feature to learn personalized ontologies

Shen, Yan, Li, Yuefeng, & Xu, Yue (2012) Adopting relevance feature to learn personalized ontologies. Lecture Notes in Computer Science, 7691, pp. 457-468.

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Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.

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ID Code: 58120
Item Type: Journal Article
Refereed: Yes
Additional Information: Paper presented in The 25th Australasian Joint Conference on Advances in Artificial Intelligence, 4-7 December 2012
Sydney Harbour Marriott Hotel
Sydney, Australia
Keywords: Relevance feature, Specificity term, Ontology, Local instance, Global Knowledge base, Concept matching
DOI: 10.1007/978-3-642-35101-3_39
ISBN: 9783642351006
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Springer-Verlag
Deposited On: 13 Mar 2013 03:02
Last Modified: 19 Jan 2015 01:48

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