Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments
This work addresses the challenge of querying inconsistent data with priorities, which is relevant for database and AI applications, but it is incremental as it builds on existing SAT-based procedures by extending them to handle general priority relations.
The paper tackled the problem of inconsistency-tolerant query answering over prioritized knowledge bases by introducing the first SAT encodings for Pareto- and completion-optimal repairs with general priority relations, and experimentally evaluated their impact and performance, showing practical algorithms for computing query answers under various semantics.
We investigate practical algorithms for inconsistency-tolerant query answering over prioritized knowledge bases, which consist of a logical theory, a set of facts, and a priority relation between conflicting facts. We consider three well-known semantics (AR, IAR and brave) based upon two notions of optimal repairs (Pareto and completion). Deciding whether a query answer holds under these semantics is (co)NP-complete in data complexity for a large class of logical theories, and SAT-based procedures have been devised for repair-based semantics when there is no priority relation, or the relation has a special structure. The present paper introduces the first SAT encodings for Pareto- and completion-optimal repairs w.r.t. general priority relations and proposes several ways of employing existing and new encodings to compute answers under (optimal) repair-based semantics, by exploiting different reasoning modes of SAT solvers. The comprehensive experimental evaluation of our implementation compares both (i) the impact of adopting semantics based on different kinds of repairs, and (ii) the relative performances of alternative procedures for the same semantics.