This work presents six structural quality metrics that can measure the quality of knowledge graphs and analyzes five cross-domain knowledge graphs on the web (Wikidata, DBpedia, YAGO, Google Knowledge Graph, Freebase) as well as 'Raftel', Naver's integrated knowledge graph. The 'Good Knowledge Graph' should define detailed classes and properties in its ontology so that knowledge in the real world can be expressed abundantly. Also, instances and RDF triples should use the classes and properties actively. Therefore, we tried to examine the internal quality of knowledge graphs numerically by focusing on the structure of the ontology, which is the schema of knowledge graphs, and the degree of use thereof. As a result of the analysis, it was possible to find the characteristics of a knowledge graph that could not be known only by scale-related indicators such as the number of classes and properties.

本研究提出了6个可衡量知识图谱质量的结构化质量度量标准，并分析了5个跨领域的知识图谱。研究结果表明，优秀的知识图谱应定义详细的类和属性以便丰富地表达现实世界的知识，并且实例和RDF三元组应积极使用这些类和属性。因此，本文通过关注本体论的结构和使用的程度，试图从定量角度研究知识图谱的内部质量。通过分析，可以找到仅通过大小相关的指标（例如类和属性的数量）无法得知的知识图谱特征。

评估知识图谱的结构质量指标