Query Answering with Inconsistent Existential Rules under Stable Model Semantics
This work addresses inconsistency handling in ontology-based data access, which is an incremental improvement for knowledge representation and reasoning systems.
The paper tackles the problem of query answering with inconsistent existential rules by introducing a rule repair framework under stable model semantics, and shows that for certain rule classes, the computational complexity remains unchanged compared to conventional semantics, with experimental results demonstrating good scalability on realistic cases.
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.