Structural Quality Metrics to Evaluate Knowledge Graph Quality

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Sumin Seo
Heeseon Cheon
Hyunho Kim
Dongseok Hyun

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Guest Editors Wikidata 2022

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This work presents six structural quality metrics that measures the quality of knowledge graphs and apply the metrics to six knowledge graphs: four cross-domain knowledge graphs on the web (Wikidata, DBpedia, YAGO, Freebase), Google Knowledge Graph, and Naver's integrated knowledge graph (Raftel). The `Good Knowledge Graph' should define specific classes and properties in its ontology so that it can abundantly express knowledge in the real world. Also, Knowledge Graph should use the classes and properties actively. We tried to examine the internal quality of knowledge graphs by focusing on the structure of the ontology, which is the schema of knowledge graphs, and the degree of use thereof. As a result, We have found the characteristics of a good knowledge graph that could not be known only by scale-related indicators such as the number of classes and properties.
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