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Ontology−Based Query Answering with Group Preferences

Thomas Lukasiewicz‚ Maria Vanina Martinez‚ Gerardo I. Simari and Oana Tifrea−Marciuska


The Web has recently been evolving into a system that is in many ways centered on social interactions and is now more and more becoming what is called the Social Semantic Web. One of the many implications of such an evolution is that the ranking of search results no longer depends solely on the structure of the interconnections among Web pages — instead, the social components must also come into play. In this paper, we argue that such rankings can be based on ontological background knowledge and on user preferences. Another aspect that has become increasingly important in recent times is that of uncertainty management, since uncertainty can arise due to many uncontrollable factors. To combine these two aspects, we propose extensions of the Datalog+/– family of ontology languages that both allow for the management of partially ordered preferences of groups of users as well as uncertainty, which is represented via a probabilistic model. We focus on answering k-rank queries in this context, presenting different strategies to compute group preferences as an aggregation of the preferences of a collection of single users. We also study merging operators that are useful for combining the preferences of the users with those induced by the values obtained from the probabilistic model. We then provide algorithms to answer k-rank queries for DAQs (disjunctions of atomic queries) under these group preferences and uncertainty that generalizes top-k queries based on the iterative computation of classical skyline answers. We show that such DAQ answering in Datalog+/– can be done in polynomial time in the data complexity, under certain reasonable conditions, as long as query answering can also be done in polynomial time (in the data complexity) in the underlying classical ontology. Finally, we present a prototype implementation of the query answering system, as well as experimental results (on the running time of our algorithms and the quality of their results) obtained from real-world ontological data and preference models, derived from information gathered from real users, showing in particular that our approach is feasible in practice.