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

Abstract

Summary 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. Availability The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. Supplementary information Supplementary data are available online.

Network graph representation of COVID-19 scientific publications to aid knowledge discovery

“…Recently there have been several initiatives to explore knowledge graphs in medical data and with some applied to aspects of COVID-19-associated published literature. 15 16 This study has demonstrated the feasibility of using a graph database approach to create a targeted concept association networks as an interactive way to allow users to easily navigate the rapidly growing COVID-19-related literature, and particularly as a way to understand and explore the relationships between key concepts within this corpus of literature articles, which is potentially widely applicable to other disease areas.…”

Network Graph Representation of COVID-19 Scientific Publications to Aid Knowledge Discovery

“…Recently there have been several initiatives to explore knowledge graphs in medical data and with some applied to aspects of COVID-19 associated published literature. [10,11] This study has demonstrated the feasibility of using a graph database approach to create a targeted concept association networks as an interactive way to allow users to easily navigate the rapidly growing COVID-19 related literature, and particularly as a way of understanding and exploring the relationships between the key concepts within this corpus of literature articles.…”

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

“…There have been a few parallel efforts to construct KGs to integrate COVID-19 data, each integrating different data sources and constructed for different purposes. Several efforts have constructed KGs by ingesting and transforming scientific literature 10 (https:// lg-covid-19-hotp.cs.duke.edu/), some with a few additional types of data also included, such as confirmed case and mortality data (https://github.com/covidgraph/); clinical information, drug trial, and sequencing data (https://www.wikidata.org/wiki/ Wikidata:WikiProject_COVID-19); drug, drug trial, and genome sequence data (https://ds-covid19.res.ibm.com/); diseases, chemicals, and genes. 11 Other KG efforts ingest a wider array of data, including diseases, genes, proteins and their structural data, drugs, and drug side effects; 12 pathways, proteins, genes, drugs, diseases, anatomic terms, phenotypes, microbiome (https://spoke.ucsf.edu/); genes, proteins, diseases, phenotypes, genome sequences 13 (https://knetminer.com/); and geographic, viral genes, genes, and proteins (https://github.com/sbl-sdsc/ coronavirus-knowledge-graph).…”