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Personalized Search for the Social Semantic Web

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 my research, I argue that such rankings can be based on user preferences from the Social Web and on ontological background knowledge from the Semantic Web, therefore I combine preference representation languages with Semantic Web technologies. Research in database community had dedicated some time to integrate preferences in database queries. In my thesis, as a first step towards closing the gap between the Semantic Web, databases, and preferences, we introduce families of expressive extensions of Datalog± with preferences as new paradigms for query answering over ontologies.We first define the syntax and semantic of the proposed frameworks, then propose a top-k query answering algorithm under user preferences in semantic data for different types of queries and preferences models. Each of our proposed frameworks comes with advantages and disadvantages therefore, we provide formal properties of our algorithms and empirical experiments on the performance and quality of our results. Furthermore, we explore the combination of our framework with uncertainty and the generalization to the preferences of a group of users, where we analyze properties of our algorithms related with social choice theory.