A Mashup Personalization Service Based on Semantic Web Rules and Linked Data

The growing availability of Linked Data and other structured information on the Web does not keep pace with the rich semantic descriptions and conceptual associations that would be necessary for direct deployment of user-tailored services. In contrast, the more complex descriptions become, the harder it is to reason about them. To show the efficacy of a potential compromise between the two, in this paper we propose an intelligent and scalable personalization service, built upon the idea of combining Linked Data with Semantic Web rules. This service is mashing up information from different bookstores, and suggests users with personalized data according to their preferences, which in turn are modeled by a set of Semantic Web rules. This information is made available as Linked Data, thus enabling third-party recipients to consume knowledge-enhanced information.