提出新指标衡量知识图谱分类定义的精细程度，发现越精细越有助于下游任务。
Class Granularity: How richly does your knowledge graph represent the real world?
- 引入'类粒度'指标，评估知识图谱中类别定义的精细程度。
- 实验表明高类粒度提升图嵌入效果，尤其在实体区分上更优。
- 首次用该指标对比四大开放数据源，超越传统规模与分布比较。
为有效管理和利用知识图谱，需从多角度评估其质量。尽管已有相关质量度量研究，但针对本体（知识图谱核心）定义丰富程度及其影响的度量仍缺乏。本文提出新指标‘类粒度’，衡量知识图谱在具有独特特征的类别定义上的精细程度。研究进一步探讨其对下游任务的影响，特别在图嵌入任务中展示实验结果。此外，本研究突破传统基于规模和类别分布的链接开放数据比较，首次使用类粒度对四个不同开放数据源进行系统对比。
原文摘要 · Abstract (English)
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.
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