Personalised search for the Social Semantic Web

Recently, the Web has 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 ranking 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. There is some related research in database community that had dedicated some time to integrate preferences in database queries. However, one cannot directly use the ideas from databases, as we additionally have ontological knowledge, which may introduce unknown values, so-called nulls. Therefore, I need to define the exact semantics and check their feasibility for this context. In my thesis, as a first step towards closing the gap between the Semantic Web, databases, and preferences, I introduce families of expressive extensions of Datalog± with preferences as new paradigms for query answering over ontologies. I first define the syntax and semantic of the proposed frameworks, then propose top-k query answering algorithms under user preferences in semantic data for different types of queries and preference models. Each of the proposed frameworks comes with advantages and disadvantages; therefore, I provide formal properties of my algorithms and empirical experiments on the performance and quality of my results. Furthermore, I explore the combination of my framework with uncertainty and the generalisation to the preferences of a group of users, where I analyse properties of my algorithms related with social choice theory.