基于图的不一致容忍语义下的查询应答方法

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

本体在演变的过程中常出现不一致性问题,这将导致经典的推理模式失效.不一致容忍语义能有效地解决推理失效的问题,但各类不一致容忍语义或者需要耗费大量计算,或者丢弃了本体中有效的信息.为此,一种针对IAR-语义和ICAR-语义的变种被用以解决上述的缺陷.新定义的IPAR-语义能够避免计算整个ABox关于TBox的封闭,在减少计算量的同时尽可能地保留了本体中的信息.在IPAR-语义下实现了基于图的查询应答方法,新方法将本体和查询以不同的规则构建成图,避免了传统重写导致的查询冗余的问题.最后,通过实验对比新的查询应答方法与ICAR-语义下的查询应答方法,实验结果表明:基于图的一致性查询方法执行效率要优于ICAR-语义下的查询方法;在本体规模不断增加的情况下,新方法具有更好的稳定性.

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.