Class Granularity: How richly does your knowledge graph represent the real world?

TL;DR

This paper introduces the Class Granularity metric to evaluate how richly ontologies are defined in knowledge graphs and explores its impact on tasks like graph embedding and data source comparison.

Contribution

It proposes a novel metric, Class Granularity, to assess the richness of ontology definitions in knowledge graphs and analyzes its influence on downstream applications.

Findings

Class Granularity correlates with embedding quality.

Higher granularity improves knowledge graph utility.

Effective for comparing Linked Open Data sources.

Abstract

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.