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Diagnosis discrimination for ontology debugging

机译:本体调试诊断鉴别

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Debugging is an important prerequisite for the widespread application of ontologies, especially in areas that rely upon everyday users to create and maintain knowledge bases, such as the Semantic Web. Recent approaches use diagnosis methods to identify sources of inconsistency. However, in most debugging cases these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit probabilities of typical user errors to formulate information theoretic concepts for query selection. Our evaluation showed that the suggested method reduces the number of required observations compared to myopic strategies.
机译:调试是对本体普遍存在应用的重要前提,尤其是在日常用户创建和维护知识库(如语义网络)的区域。最近的方法使用诊断方法来识别不一致的源。但是,在大多数调试情况下,这些方法返回许多替代诊断,从而将故障定位的负担放在用户上。本文演示了如何通过执行一系列观察序列来识别目标诊断,即通过查询Oracle关于目标本体的征兆。我们利用典型用户错误的概率来制定查询选择的信息理论概念。我们的评价表明,与近视策略相比,建议的方法减少了所需观察的数量。

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