  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).
