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).

在这篇论文中，我们对来自各个领域（如自然科学、医学和自然语言处理）的 29 个真实知识图谱数据集进行了大规模比较研究，分析了它们的属性和结构模式，并根据发现提出了针对基于知识图谱模型开发和评估的几项建议。我们相信知识图谱中丰富的结构信息可以有益于各个领域更好地发展知识图谱模型，并希望这项研究能够有助于打破现有领域间的数据孤岛（例如机器学习、自然语言处理和科学人工智能之间的联系）。