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Traditional inconsistency-tolerent query answering in ontology-based dataaccess relies on selecting maximal components of an ABox/database which areconsistent with the ontology. However, some rules in ontologies might beunreliable if they are extracted from ontology learning or written byunskillful knowledge engineers. In this paper we present a framework ofhandling inconsistent existential rules under stable model semantics, which isdefined by a notion called rule repairs to select maximal components of theexistential rules. Surprisingly, for R-acyclic existential rules withR-stratified or guarded existential rules with stratified negations, both thedata complexity and combined complexity of query answering under the rule{repair semantics} remain the same as that under the conventional queryanswering semantics. This leads us to propose several approaches to handle therule {repair semantics} by calling answer set programming solvers. Anexperimental evaluation shows that these approaches have good scalability ofquery answering under rule repairs on realistic cases.

Query Answering with Inconsistent Existential Rules under Stable Model Semantics