论文标题
评估知识图的结构质量指标
Structural Quality Metrics to Evaluate Knowledge Graphs
论文作者
论文摘要
这项工作介绍了六个结构质量指标，可以测量知识图的质量，并分析网络上的五个跨域知识图（Wikidata，dbpedia，Yago，Yago，Google知识图，FreeBase）以及Naver的集成知识图。 “良好的知识图”应在其本体论中定义详细的类和属性，以便可以充分表达现实世界中的知识。同样，实例和RDF三元组应积极使用类和属性。因此，我们试图通过关注本体论的结构，即知识图的模式及其使用程度来检查知识图的内部质量。分析的结果是，可以找到知识图的特征，而知识图的特征只能通过与尺度相关的指标（例如类和属性的数量）来知道。
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.
