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A Multi-reasoner, Justification-Based Approach to Reasoner Correctness

机译:一种多推理的,基于理由的理发师正确性方法

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OWL 2 DL is a complex logic with reasoning problems that have a high worst case complexity. Modern reasoners perform mostly very well on naturally occurring ontologies of varying sizes and complexity. This performance is achieved through a suite of complex optimisations (with complex interactions) and elaborate engineering. While the formal basis of the core reasoner procedures are well understood, many optimisations are less so, and most of the engineering details (and their possible effect on reasoner correctness) are unreviewed by anyone but the reasoner developer. Thus, it is unclear how much confidence should be placed in the correctness of implemented reasoners. To date, there is no principled, correctness unit test-like suite for simple language features and, even if there were, it is unclear that passing such a suite would say much about correctness on naturally occurring ontologies. This problem is not merely theoretical: Divergence in behaviour (thus known bugginess of implementations) has been observed in the OWL Reasoner Evaluation (ORE) contests to the point where a simple, majority voting procedure has been put in place to resolve disagreements. In this paper, we present a new technique for finding and resolving reasoner disagreement. We use justifications to cross check disagreements. Some cases are resolved automatically, others need to be manually verified. We evaluate the technique on a corpus of naturally occurring ontologies and a set of popular reasoners. We successfully identify several correctness bugs across different reasoners, identify causes for most of these, and generate appropriate bug reports and patches to ontologies to work around the bug.
机译:OWL DL 2与推理具有高最坏情况下的复杂性问题复杂的逻辑。现代推理上自然产生的不同大小和复杂性的本体进行大多非常好。这一业绩是通过一系列复杂的优化(有复杂的相互作用),并精心制作的工程实现。虽然核心推理程序正式的基础是很好理解的,很多的优化要少一些,而且大部分的工程细节(和推理的正确性及其可能的影响)由任何人,但推理开发商未审核。因此,目前还不清楚有多少信心应该放在实现推理的正确性。迄今为止,没有条理化,正确性单元测试样套件简单的语言特性,并且即使有,目前还不清楚这种通过一套房会说很多关于自然发生的本体论的正确性。这个问题不仅是理论:分歧的行为在OWL推理机评价(矿)场比赛到一个简单的,多数表决程序已经到位,以解决分歧点被观察到(因此称为实现的Bug多多)。在本文中,我们提出了发现和解决分歧推理的新技术。我们使用的理由来交叉检查的分歧。某些情况下自动解决,有些则需要手动验证。我们评估对自然产生的本体的语料库和一组流行的推理的技术。我们成功地识别出几个正确性错误在不同的推理,发现大多数的这些原因,并生成相应的错误报告和补丁本体要解决的bug。

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