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.
翻译:这项工作提出了六种结构质量指标,可以衡量知识图的质量,分析网络上的五种跨域知识图(维基数据、DBpedia、YAGO、Google知识图、Freebase)以及“Raftel”和Naver的综合知识图。“良好知识图”应该在其本科学中界定详细的类别和属性,以便能充分表达真实世界的知识。此外,实例和RDF的三倍应该积极地使用这些类别和属性。因此,我们试图通过注重本科学结构(即知识图的形态)及其使用程度,从数字上审查知识图的内部质量。通过分析,可以找到知识图的特征,而该特征不能仅仅通过与规模有关的指标(如类别和属性的数量)来知晓。