The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application’s supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator’s performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation “Validatrr”, based on N3Logic and the EYE reasoner. Our approach – supporting an equivalent number of constraint types compared to the state of the art – better explains the root cause of the violations due to the reasoner’s generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions.

中文翻译：

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使用基于规则的推理进行RDF图验证

语义Web应用程序的正确功能要求给定的RDF图遵守预期的形状。这种形状取决于RDF图和该图的应用程序支持的内容。在验证期间，将对照约束集评估RDF图，发现违规将有助于完善RDF图。但是，现有的验证方法不能总是解释违规的根本原因（禁止改进），并且不能将验证过程中支持的内容与应用程序支持的内容完全匹配。这些方法无法准确地验证RDF图，或无法组合多个系统，从而降低了验证器的性能。在本文中，我们提出了一种使用基于规则的推理的替代验证方法，该方法能够完全自定义使用的推理步骤。我们与现有方法进行了比较，并基于N3Logic和EYE推理器提出了正式的基础和实际的实现“ Validatrr”。我们的方法（与现有技术相比，支持相等数量的约束类型）更好地解释了由于推理机生成的逻辑证明而引起的违规的根本原因，并由于可定制的推理规则集而返回了准确数量的违规。性能评估表明Validatrr对于较小的数据集具有较高的性能，并且可以随RDF图的大小线性缩放。详细的根本原因说明可以指导将来的验证报告说明规范，并且可以使用细粒度的配置来支持不同的约束语言。这个基础可以进一步研究处理递归的方法，