Adopting relevance feature to learn personalized ontologies

Authors:
Yan Shen;Yuefeng Li;Yue Xu
Affiliations:
Electrical Engineering, Computer Science, Science and Engineering Faculty, Queensland University of Technology, Australia;Electrical Engineering, Computer Science, Science and Engineering Faculty, Queensland University of Technology, Australia;Electrical Engineering, Computer Science, Science and Engineering Faculty, Queensland University of Technology, Australia
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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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.