Pushing Optimal ABox Repair from EL Towards More Expressive Horn-DLs

F. Baader, Francesco Kriegel
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引用次数: 5

Abstract

Ontologies based on Description Logic (DL) represent general background knowledge in a terminology (TBox) and the actual data in an ABox. DL systems can then be used to compute consequences (such as answers to certain queries) from an ontology consisting of a TBox and an ABox. Since both human-made and machine-learned data sets may contain errors, which manifest themselves as unintuitive or obviously incorrect consequences, repairing DL-based ontologies in the sense of removing such unwanted consequences is an important topic in DL research. Most of the repair approaches described in the literature produce repairs that are not optimal, in the sense that they do not guarantee that only a minimal set of consequences is removed. In a series of papers, we have developed an approach for computing optimal repairs, starting with the restricted setting of an EL instance store, extending this to the more general setting of a quantified ABox (where some individuals may be anonymous), and then adding a static EL TBox. Here, we extend the expressivity of the underlying DL considerably, by adding nominals, inverse roles, regular role inclusions and the bottom concept to EL, which yields a fragment of the well-known DL Horn-SROIQ. The ideas underlying our repair approach still apply to this DL, though several non-trivial extensions are needed to deal with the new constructors and axioms. The developed repair approach can also be used to treat unwanted consequences expressed by certain conjunctive queries or regular path queries, and to handle Horn-ALCOI TBoxes with regular role inclusions.
推动最佳ABox修复从EL到更富有表现力的喇叭- dl
基于描述逻辑(DL)的本体在术语(TBox)中表示一般背景知识,在ABox中表示实际数据。然后,深度学习系统可以用于从由TBox和ABox组成的本体中计算结果(例如某些查询的答案)。由于人工和机器学习的数据集都可能包含错误,这些错误表现为不直观或明显不正确的结果,因此从消除这些不需要的结果的意义上修复基于DL的本体是DL研究中的一个重要主题。文献中描述的大多数修复方法产生的修复不是最优的,因为它们不能保证只有最小的结果集被消除。在一系列论文中,我们开发了一种计算最佳修复的方法,从EL实例存储的受限设置开始,将其扩展到更一般的量化ABox设置(其中一些个体可能是匿名的),然后添加静态EL TBox。在这里,我们通过向EL添加标称、逆角色、正则角色包含和底部概念,大大扩展了底层DL的表达能力,从而产生了著名的DL Horn-SROIQ的片段。我们修复方法的基础思想仍然适用于这个DL,尽管需要几个重要的扩展来处理新的构造函数和公理。所开发的修复方法还可用于处理由某些连接查询或常规路径查询表达的不希望的结果,并处理具有常规角色包含的Horn-ALCOI tbox。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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