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. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Retrieval models General Terms: Algorithm, Performance

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Xing Jiang, Ah-Hwee Tan
• Keywords: Internet Technology, Ontology Based User, Semantic-based User Ontology, User Ontology Model, WWW 2006