Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGs
This study addresses the problem of data silos and understudied KG properties for researchers in ML, NLP, and AI, though it is incremental as it focuses on analysis rather than new methods.
The authors tackled the lack of understanding of real knowledge graph (KG) structures by conducting a large-scale comparative study of 29 KG datasets from diverse domains, leading to recommendations for model development and evaluation.
Despite the recent popularity of knowledge graph (KG) related tasks and benchmarks such as KG embeddings, link prediction, entity alignment and evaluation of the reasoning abilities of pretrained language models as KGs, the structure and properties of real KGs are not well studied. In this paper, we perform a large scale comparative study of 29 real KG datasets from diverse domains such as the natural sciences, medicine, and NLP to analyze their properties and structural patterns. Based on our findings, we make several recommendations regarding KG-based model development and evaluation. We believe that the rich structural information contained in KGs can benefit the development of better KG models across fields and we hope this study will contribute to breaking the existing data silos between different areas of research (e.g., ML, NLP, AI for sciences).