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Adopting Relevance Feature to Learn Personalized Ontologies
Yan Shen, Yuefeng Li, and Yue Xu
Electrical Engineering, Computer Science, Science and Engineering Faculty, Queensland University of Technology, Australiay12.shen@qut.edu.au
y2.li@qut.edu.au
yue.xu@qut.edu.au
Abstract. 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.
Keywords: Relevance Feature, Specificity Term, Ontology, Local Instance, Global Knowledge Base, Concept Matching
LNAI 7691, p. 457 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012