Reasoning with $$\mathcal {SROIQ(D)}$$SROIQ(D), the logic that underpins the popular Web Ontology Language (OWL), has a high worst case complexity (N2Exptime). Decomposing the ontology into modules prior to classification, and then classifying the composites one-by-one, has been suggested as a way to mitigate this complexity in practice. Modular reasoning is currently motivated by the potential for reducing the hardness of subsumption tests, reducing the number of necessary subsumption tests and integrating efficient delegate reasoners. To date, we have only a limited idea of what we can expect from modularity as an optimisation technique. We present sound evidence that, while the impact of subsumption testing is significant only for a small number of ontologies across a popular collection of 330 ontologies (BioPortal), modularity has a generally positive effect on subsumption test hardness (2-fold mean reduction in our sample). More than 50% of the tests did not change in hardness at all, however, and we observed large differences across reasoners. We conclude (1) that, in general, optimisations targeting subsumption test hardness need to be well motivated because of their comparatively modest overall impact on classification time and (2) that employing modularity for optimisation should not be motivated by beneficial effects on subsumption test hardness alone.

中文翻译：

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OWL 推理：包含测试硬度和模块化

使用 $$\mathcal {SROIQ(D)}$$SROIQ(D) 推理，支持流行的 Web 本体语言 (OWL) 的逻辑具有很高的最坏情况复杂度 (N2Exptime)。在分类之前将本体分解为模块，然后对组合进行一个一个的分类，已被建议作为在实践中减轻这种复杂性的一种方法。模块化推理目前的动机是降低包含测试的难度，减少必要的包含测试的数量和集成高效的委托推理器。迄今为止，我们对模块化作为一种​​优化技术的期望只有有限的想法。我们提供了可靠的证据，虽然包含测试的影响仅对 330 个流行的本体集合（BioPortal）中的少数本体显着，模块化对包容性测试硬度具有普遍积极的影响（我们的样本平均减少了 2 倍）。然而，超过 50% 的测试的硬度根本没有变化，我们观察到推理机之间的巨大差异。我们得出结论：（1）一般来说，针对包含测试硬度的优化需要有很好的动机，因为它们对分类时间的总体影响相对较小；（2）采用模块化进行优化不应受到对包含测试硬度的有益影响的动机独自的。