  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.
