Web Usage Mining for Semantic Web Personalization

With the explosive growth of information on the Web, it has become more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In Semantic Web, user access behavior models can be shared as ontology. Agent software can then utilize it to provide personalized services such as recommendation and search. To achieve this, we need to tackle the technical issues on transforming web access activities into ontology, and deducing personalized usage knowledge from the ontology. In this paper, we propose a web usage mining approach for semantic web personalization. The proposed approach first incorporates fuzzy logic into Formal Concept Analysis to mine user access data for automatic ontology generation, and then applies approximate reasoning to generate personalized usage knowledge from the ontology for providing personalized services.