Query Answering with Inconsistent Existential Rules under Stable Model
  Semantics
  Traditional inconsistency-tolerent query answering in ontology-based data
access relies on selecting maximal components of an ABox/database which are
consistent with the ontology. However, some rules in ontologies might be
unreliable if they are extracted from ontology learning or written by
unskillful knowledge engineers. In this paper we present a framework of
handling inconsistent existential rules under stable model semantics, which is
defined by a notion called rule repairs to select maximal components of the
existential rules. Surprisingly, for R-acyclic existential rules with
R-stratified or guarded existential rules with stratified negations, both the
data complexity and combined complexity of query answering under the rule
{repair semantics} remain the same as that under the conventional query
answering semantics. This leads us to propose several approaches to handle the
rule {repair semantics} by calling answer set programming solvers. An
experimental evaluation shows that these approaches have good scalability of
query answering under rule repairs on realistic cases.
