Learning and Inferencing in User Ontology for Personalized Semantic Web Services
Domain ontology has been used in many Semantic Web applications. However, few applications explore the use of ontology for personalized services. This paper proposes an ontology based user model consisting of both concepts and semantic relations to represent users' interests. Specifically, we adopt a statistical approach to learning a semantic-based user ontology model from domain ontology and a spreading activation procedure for inferencing in the user ontology model. We apply the methods of learning and exploiting user ontology to a semantic search engine for finding academic publications. Our experimental results support the efficacy of user ontology and spreading activation theory (SAT) for providing personalized semantic services.
Jiang, X. and Tan, A. 2006. Learning and inferencing in user ontology for personalized semantic web services. In Proceedings of the 15th International Conference on World Wide Web (Edinburgh, Scotland, May 23 - 26, 2006). WWW '06. ACM Press, New York, NY, 1067-1068.
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