Ontological Query Answering under Many−Valued Group Preferences in Datalog+⁄−

Abstract

The Web has recently been changing more and more to what is called the Social Semantic Web. As a consequence, the ranking of search results no longer depends solely on the structure of the interconnections among Web pages. In this paper, we argue that such rankings can be based on user preferences from the Social Web and on ontological back-ground knowledge from the Semantic Web. We propose an approach to top-k query answering under user preferences in Datalog+/– ontologies, where the queries are unions of conjunctive queries with safe negation, and the preferences are defined via numerical values. To this end, we also generalize the previous RankJoin algorithm to our framework. Furthermore, we explore the generalization to the preferences of a group of users. Finally, we provide experimental results on the performance and quality of our algorithms.