Reasoning with Semantic-Enabled Qualitative Preferences.

Personalized access to information is an important task in all real-world applications where the user is interested in documents, items, objects or data that match her preferences. Among qualitative approaches to preference representation, CP-nets play a prominent role: with their clear graphical structure, they unify an easy representation of user desires with nice computational properties when computing the best outcome. In this paper, we explore how to reason with CP-nets in the context of the Semantic Web, where preferences are linked to formal ontologies. We show how to compute Pareto optimal outcomes for a semantic-enabled CP-net by solving a constraint satisfaction problem, and we present complexity results related to different ontological languages.