Learning and Inferencing in User Ontology for Personalized Semantic Web Services

• Xing Jiang, Nanyang Technological University, Singapore
• Ah-Hwee Tan, Nanyang Technological University, Singapore

Full text:

Track: Posters

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.

Other items being presented by these speakers

• Mining RDF Metadata for Generalized Association Rules: Knowledge Discovery in the Semantic Web Era (Posters Track)