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This paper investigates the use of Answer Set Programming (ASP) and ASP(Q) for querying inconsistent prioritized data by defining three notions of optimal repairs: Pareto-, globally-, and completion-optimal. It considers variants of AR, brave, and IAR semantics using these repairs, achieving query answering within the first or second level of the polynomial hierarchy for a broad class of logical theories. The work introduces the first implementation of globally-optimal repair-based semantics and a tractable grounded semantics approximation, experimentally evaluating their feasibility and impact.
Finally, a practical implementation for globally-optimal repair-based semantics allows for querying inconsistent prioritized data with theoretical guarantees.
We explore the use of answer set programming (ASP) and its extension with quantifiers, ASP(Q), for inconsistency-tolerant querying of prioritized data, where a priority relation between conflicting facts is exploited to define three notions of optimal repairs (Pareto-, globally- and completion-optimal). We consider the variants of three well-known semantics (AR, brave and IAR) that use these optimal repairs, and for which query answering is in the first or second level of the polynomial hierarchy for a large class of logical theories. Notably, this paper presents the first implementation of globally-optimal repair-based semantics, as well as the first implementation of the grounded semantics, which is a tractable under-approximation of all these optimal repair-based semantics. Our experimental evaluation sheds light on the feasibility of computing answers under globally-optimal repair semantics and the impact of adopting different semantics, approximations, and encodings.
