The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats.

中文翻译：

---

COVID-19 知识图：COVID-19 病理生理学的可计算、多模态、因果关系知识模型

COVID-19 危机引起了科学界的全球响应，导致有关该病毒病理生理学的出版物大量涌现。然而，如果没有协调努力来组织这些知识，它可能会隐藏在各个研究小组之外。通过以结构化和可计算的形式（如知识图的形式）提取和形式化这些知识，研究人员可以很容易地在更大范围内推理和分析这些信息。在这里，我们展示了 COVID-19 知识图谱，这是一个由关于新型冠状病毒的科学文献构建的广泛的因果网络，旨在提供对其病理生理学的全面了解。为了使该资源可供研究界使用并促进其探索和分析，