Preference representation and reasoning is a key issue in many real-world scenarios where a personalized access to information is needed. Currently,there are many approaches allowing a system to assess preferences in a qualita-tive or quantitative way, and among the qualitative ones the most prominent areCP-nets. Their clear graphical structure unifies an easy representation of user de-sires with nice computational properties when computing the best outcome. Inthis paper, we show how to reason with CP-nets when the attributes modeling theknowledge domain have an ontological structure or, in other words, variable val-ues are DL formulas constrained relative to an underlying domain ontology. Wealso show how the computation of Pareto-optimal outcomes for an ontologicalCP-net can be reduced to the solution of constraint satisfaction problems
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