Debugging Classical Ontologies Using Defeasible Reasoning Tools

Simone Coetzer, K. Britz
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引用次数: 1

Abstract

A successful application of ontologies relies on representing as much accurate and relevant domain knowledge as possible, while maintaining logical consistency. As the successful implementation of a real-world ontology is likely to contain many concepts and intricate relationships between the concepts, it is necessary to follow a methodology for debugging and refining the ontology. Many ontology debugging approaches have been developed to help the knowledge engineer pinpoint the cause of logical inconsistencies and rectify them in a strategic way. We show that existing debugging approaches can lead to unintuitive results, which may lead the knowledge engineer to opt for deleting potentially crucial and nuanced knowledge. We provide a methodological and design foundation for weakening faulty axioms in a strategic way using defeasible reasoning tools. Our methodology draws from Rodler’s interactive ontology debugging approach and extends this approach by creating a methodology to systematically find conflict resolution recommendations. Importantly, our goal is not to convert a classical ontology to a defeasible ontology. Rather, we use the definition of exceptionality of a concept, which is central to the semantics of defeasible description logics, and the associated algorithm to determine the extent of a concept’s exceptionality (their ranking); then, starting with the statements containing the most general concepts (the least exceptional concepts) weakened versions of the original statements are constructed; this is done until all inconsistencies have been resolved.
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使用可失败推理工具调试经典本体
本体的成功应用依赖于表示尽可能准确和相关的领域知识,同时保持逻辑一致性。由于现实世界本体的成功实现可能包含许多概念和概念之间复杂的关系,因此有必要遵循一种调试和改进本体的方法。已经开发了许多本体调试方法来帮助知识工程师查明逻辑不一致的原因,并以战略的方式纠正它们。我们表明,现有的调试方法可能导致不直观的结果,这可能导致知识工程师选择删除潜在的关键和微妙的知识。我们提供了一个方法和设计基础,以削弱错误的公理在一个战略的方式,使用可行的推理工具。我们的方法借鉴了Rodler的交互式本体调试方法,并通过创建一种方法来系统地找到解决冲突的建议,从而扩展了该方法。重要的是,我们的目标不是将经典本体转换为可推翻的本体。相反,我们使用概念的异常定义,这是可否定描述逻辑语义的核心,以及相关算法来确定概念的异常程度(它们的排名);然后,从包含最一般概念(最少例外概念)的语句开始,构建原始语句的弱化版本;直到所有不一致的问题都得到解决为止。
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