SemRPer - A Rule based Personalization System for Semantic Web

Personalization is the adaptation of information according to user needs. Personalization when applied to Semantic Web offers many advantages when compared to traditional Web as Semantic Web is the next generation Web. Ontologies play a crucial role in bringing the Semantic Web to its full potential. Ontological relationship can be utilized in inferring additional information in Semantic Web. In this paper, a SemRPer rule based personalization system for Semantic Web is proposed. SemRPer system generates personalized recommendations using a generic rule reasoner. Generic rule reasoner is built on the rule set which consist of a set of rules written to carry out the personalization task. Performance of SemRPer system is demonstrated by using two applications as examples.