COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

Area of Science:

• Virology
• Bioinformatics
• Computational Biology

Background:

• The COVID-19 crisis spurred extensive research into the novel coronavirus's pathophysiology.
• Vast amounts of scientific literature on COVID-19 pathophysiology exist but are fragmented.
• Organizing this knowledge is crucial for large-scale analysis and research advancement.

Purpose of the Study:

• To construct a comprehensive, cause-and-effect knowledge graph of COVID-19 pathophysiology.
• To provide a structured, computable resource for researchers.
• To facilitate the exploration and analysis of COVID-19-related scientific knowledge.

Main Methods:

• Extraction of information from scientific literature on the novel coronavirus.
• Formalization of extracted knowledge into a structured, computable knowledge graph (KG).
• Development of a web application for KG exploration and analysis.

Main Results:

• Creation of the COVID-19 Knowledge Graph, an expansive cause-and-effect network.
• The KG provides a comprehensive overview of the virus's pathophysiology.
• The resource is accessible via a web application and multiple standard formats.

Conclusions:

• The COVID-19 Knowledge Graph consolidates fragmented knowledge for researchers.
• This structured resource enables large-scale reasoning and analysis of COVID-19 pathophysiology.
• The KG promotes collaborative research and accelerates understanding of the novel coronavirus.