A Graph-Based Approach for Query Answering Under Inconsistency-Tolerant Semantics

Inconsistency often occurs during ontology evolution, and leads to the invalidity of standard reasoning. To tackle this problem, inconsistency-tolerant semantics can be provided for the target language. However, ill-defined inconsistency-tolerant semantics may cost massive calculation and result in losing valuable information. In this paper, a variant of classical inconsistency-tolerant semantics is proposed, named IPAR-semantics. The newly defined inconsistency-tolerant semantics can avoid computing the closure of an ABox w.r.t. the corresponding TBox, thus can reduce the computation time and reserve as much information as possible. Based on the newly defined inconsistency-tolerant semantics, we further propose an approach for consistent query answering based on graph. In our approach, the given ontology and the target query are both transformed into graphs by different rules and stored into graph database. The IPAR-semantics ensure that the inconsistent instances cannot be included in the answering of query and the new approach can avoid redundant rewritings of a user query. Finally, We conduct comparative experiments on the ontologies generated by UOBM generator. In the experiments, we implement the query answering system under IPAR-semantics using our graph-based approach and compare it with the query answering approach under ICAR-semantics. The experimental results show that our approach outperforms in both efficiency and scalability.