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Consequence-Based Reasoning for Description Logics with Disjunctions and Number Restrictions

机译:基于后果的描述逻辑与剖钉和数量限制的推理

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Classification of description logic (DL) ontologies is a key computational problem in modern data management applications, so considerable effort has been devoted to the development and optimisation of practical reasoning calculi. Consequence-based calculi combine ideas from hypertableau and resolution in a way that has proved very effective in practice. However, existing consequence-based calculi can handle either Horn DLs (which do not support disjunction) or DLs without number restrictions. In this paper, we overcome this important limitation and present the first consequence-based calculus for deciding concept subsumption in the DL ALCHIQ(+). Our calculus runs in exponential time assuming unary coding of numbers, and on ELH ontologies it runs in polynomial time. The extension to disjunctions and number restrictions is technically involved: we capture the relevant consequences using first-order clauses, and our inference rules adapt paramodulation techniques from first-order theorem proving. By using a well-known preprocessing step, the calculus can also decide concept subsumptions in SRIQ a rich DL that covers all features of OWL 2 DL apart from nominals and datatypes. We have implemented our calculus in a new reasoner called Sequoia. We present the architecture of our reasoner and discuss several novel and important implementation techniques such as clause indexing and redundancy elimination. Finally, we present the results of an extensive performance evaluation, which revealed Sequoia to be competitive with existing reasoners. Thus, the calculus and the techniques we present in this paper provide an important addition to the repertoire of practical implementation techniques for description logic reasoning.
机译:描述逻辑(DL)本体的分类是现代数据管理应用中的关键计算问题,因此致力于实际推理计算的开发和优化的大量努力。基于后果的Calculi在实践中证明非常有效的方式,结合了高度高度和解的想法。然而,现有的基于后果的Calculi可以处理喇叭DLS(不支持差异)或DLS,没有数字限制。在本文中,我们克服了这个重要的限制,并提出了用于决定DL ALCHIQ(+)中的概念上限的基于后果的微积分。我们的微积分在指数时间内运行,假设数组编码,并且在elh本体上运行多项式时间。技术上涉及到崩溃和数量限制的延伸:我们使用一阶条文捕获相关后果,以及我们推断规则从一阶定理证明的方法调整发作技术。通过使用众所周知的预处理步骤,微积分还可以在SRIQ中决定富裕的DL中的概念上限,涵盖猫头鹰2 dl的所有特征与标称和数据类型。我们在一个名为SemoIa的新推理中实施了我们的微积分。我们介绍了我们的推理架构,并讨论了几种新颖和重要的实现技术,如子句索引和冗余消除。最后,我们展示了广泛的绩效评估结果,揭示了随着现有资料竞争的竞争力。因此,在本文中存在的微积分和技术提供了对描述逻辑推理的实际实现技术的reptoIre的重要补充。

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