Query Answering with Inconsistent Existential Rules under Stable Model Semantics

TL;DR

This paper introduces a framework for query answering with inconsistent existential rules under stable model semantics, ensuring complexity remains manageable and demonstrating scalable solutions through answer set programming.

Contribution

It proposes a novel rule repair-based approach for handling inconsistencies in existential rules, maintaining complexity and enabling practical implementations.

Findings

Complexity remains unchanged under rule repair semantics for certain rule classes.

Answer set programming solvers effectively handle query answering with rule repairs.

Experimental results show good scalability on realistic datasets.

Abstract

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.