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RDF graph validation using rule-basedreasoning

机译:RDF图使用规则致力学验证

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摘要

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.
机译:语义Web应用程序的正确运行要求给定的RDF图符合预期的形状。这个形状取决于RDF图和应用程序支持的该图的蕴涵。在验证过程中,根据一组约束对RDF图进行评估,发现违规有助于优化RDF图。然而,现有的验证方法无法始终解释违规的根本原因(抑制细化),也无法将验证期间支持的要求与应用程序支持的要求完全匹配。这些方法无法准确地验证RDF图,或者组合多个系统,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,能够完全定制使用的推理步骤。我们比较了现有的方法,并提出了一种基于N3Logic和EYE推理器的正式基础和实际实现“Validatrr”。我们的方法——?与现有技术相比,支持同等数量的约束类型更好地解释由于推理器生成的逻辑证明而导致的违规的根本原因,并返回由于可自定义推断规则集而导致的准确违规次数。性能评估表明Validatrr对于较小的数据集是有效的,并且可以线性扩展RDF图的大小。详细的根本原因解释可以指导未来的验证报告描述规范,细粒度级别的配置可以用于支持不同的约束语言。该基础允许进一步处理递归,验证基于它们的生成描述的RDF图,并提供自动细化建议。

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