Abstract

Knowledge Graphs (KGs) have proliferated on the Web since the introduction of knowledge panels to Google search in 2012. KGs are large data-first graph databases with weak inference rules and weakly-constraining data schemes. SHACL, the Shapes Constraint Language, is a W3C recommendation for expressing constraints on graph data as shapes. SHACL shapes serve to validate a KG, to underpin manual KG editing tasks, and to offer insight into KG structure. Often in practice, large KGs have no available shape constraints and so cannot obtain these benefits for ongoing maintenance and extension.

We introduce Inverse Open Path (IOP) rules, a predicate logic formalism which presents specific shapes in the form of paths over connected entities that are present in a KG. IOP rules express simple shape patterns that can be augmented with minimum cardinality constraints and also used as a building block for more complex shapes, such as trees and other rule patterns. We define formal quality measures for IOP rules and propose a novel method to learn high-quality rules from KGs. We show how to build high-quality tree shapes from the IOP rules. Our learning method, SHACLearner, is adapted from a state-of-the-art embedding-based open path rule learner (Oprl).

We evaluate SHACLearner on some real-world massive KGs, including YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts). The experiments show that our SHACLearner can effectively learn informative and intuitive shapes from massive KGs. The shapes are diverse in structural features such as depth and width, and also in quality measures that indicate confidence and generality.

中文翻译：

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从知识图谱中学习 SHACL 形状

摘要

自 2012 年将知识面板引入 Google 搜索以来，知识图 (KG) 在 Web 上激增。KG 是大型数据优先图数据库，具有弱推理规则和弱约束数据方案。SHACL，Shapes Constraint Language，是 W3C 的推荐标准，用于将图形数据的约束表达为形状。SHACL 形状用于验证 KG，支持手动 KG 编辑任务，并提供对 KG 结构的洞察。通常在实践中，大型 KG 没有可用的形状约束，因此无法获得持续维护和扩展的这些好处。

我们引入了反向开放路径 (IOP) 规则，这是一种谓词逻辑形式主义，它以 KG 中存在的连接实体的路径形式呈现特定形状。IOP 规则表示简单的形状模式，可以使用最小基数约束进行扩充，也可以用作更复杂形状（例如树和其他规则模式）的构建块。我们为 IOP 规则定义了正式的质量度量，并提出了一种从 KGs 学习高质量规则的新方法。我们展示了如何根据 IOP 规则构建高质量的树形。我们的学习方法SHACLearner改编自最先进的基于嵌入的开放路径规则学习器 ( Oprl )。

我们在一些真实世界的大规模 KGs 上评估SHACLearner，包括 YAGO2s（400 万个事实）、DBpedia 3.8（1100 万个事实）和维基数据（800 万个事实）。实验表明，我们的SHACLearner可以有效地从大量 KG 中学习信息丰富且直观的形状。这些形状在结构特征（例如深度和宽度）以及表明置信度和普遍性的质量度量方面各不相同。