Knowledge graphs have emerged as expressive data structures for Web data. Knowledge graph potential and the demand for ecosystems to facilitate their creation, curation, and understanding, is testified in diverse domains, e.g., biomedicine. The Shapes Constraint Language (SHACL) is the W3C recommendation language for integrity constraints over RDF knowledge graphs. Enabling quality assements of knowledge graphs, SHACL is rapidly gaining attention in real-world scenarios. SHACL models integrity constraints as a network of shapes, where a shape contains the constraints to be fullfiled by the same entities. The validation of a SHACL shape schema can face the issue of tractability during validation. To facilitate full adoption, efficient computational methods are required. We present Trav-SHACL, a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. Trav-SHACL reorders the shapes in a shape schema for efficient validation and rewrites target and constraint queries for the fast detection of invalid entities. Trav-SHACL is empirically evaluated on 27 testbeds executed against knowledge graphs of up to 34M triples. Our experimental results suggest that Trav-SHACL exhibits high performance gradually and reduces validation time by a factor of up to 28.93 compared to the state of the art.

中文翻译：

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Trav-SHACL：有效验证SHACL约束的网络

知识图已经成为Web数据的表达性数据结构。知识图谱的潜力以及对生态系统以促进其创建，管理和理解的需求，已在不同领域得到了证明，例如生物医学。形状约束语言（SHACL）是W3C建议语言，用于RDF知识图上的完整性约束。通过启用知识图的质量评估，SHACL在现实世界中迅速受到关注。SHACL将完整性约束建模为形状网络，其中形状包含要由同一实体完整归档的约束。SHACL形状模式的验证在验证过程中可能会遇到易处理性问题。为了促进全面采用，需要有效的计算方法。我们介绍Trav-SHACL，SHACL引擎能够以尽早检测到无效实体并最大程度地减少不必要的验证的方式，计划形状模型的遍历和执行。Trav-SHACL对形状架构中的形状进行重新排序以进行有效验证，并重写目标查询和约束查询以快速检测无效实体。Trav-SHACL在27个测试平台上进行了经验评估，并针对多达3400万个三元组的知识图进行了测试。我们的实验结果表明，与现有技术相比，Trav-SHACL逐渐展现出高性能，并且将验证时间减少了多达28.93倍。Trav-SHACL对形状架构中的形状进行重新排序以进行有效验证，并重写目标查询和约束查询以快速检测无效实体。Trav-SHACL在27个测试平台上进行了经验评估，并针对多达3400万个三元组的知识图进行了测试。我们的实验结果表明，与现有技术相比，Trav-SHACL逐渐展现出高性能，并且将验证时间减少了多达28.93倍。Trav-SHACL对形状架构中的形状进行重新排序以进行有效验证，并重写目标查询和约束查询以快速检测无效实体。Trav-SHACL在27个测试平台上进行了经验评估，并针对多达3400万个三元组的知识图进行了测试。我们的实验结果表明，与现有技术相比，Trav-SHACL逐渐展现出高性能，并且将验证时间减少了多达28.93倍。