To effectively manage and utilize knowledge graphs, it is crucial to have metrics that can assess the quality of knowledge graphs from various perspectives. While there have been studies on knowledge graph quality metrics, there has been a lack of research on metrics that measure how richly ontologies, which form the backbone of knowledge graphs, are defined or the impact of richly defined ontologies. In this study, we propose a new metric called Class Granularity, which measures how well a knowledge graph is structured in terms of how finely classes with unique characteristics are defined. Furthermore, this research presents potential impact of Class Granularity in knowledge graph's on downstream tasks. In particular, we explore its influence on graph embedding and provide experimental results. Additionally, this research goes beyond traditional Linked Open Data comparison studies, which mainly focus on factors like scale and class distribution, by using Class Granularity to compare four different LOD sources.

本研究解决了知识图谱质量评估中缺乏对本体定义丰富性度量的问题。我们提出了一种新度量“类粒度”，用于衡量知识图谱中独特特征的类定义的精细程度。研究结果表明，类粒度对下游任务如图嵌入有显著影响，推动了知识图谱的结构优化。

类粒度：您的知识图谱如何丰富地表示现实世界？