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COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology
Preprint
in English
| bioRxiv
| ID: ppbiorxiv-040667
Journal article
A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See journal article
ABSTRACT
SummaryThe past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet 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. AvailabilityThe COVID-19 Knowledge Graph is publicly available under CC-0 license at https//github.com/covid19kg and https//bikmi.covid19-knowledgespace.de. Contactalpha.tom.kodamullil@scai.fraunhofer.de Supplementary informationSupplementary data are available online.
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Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Type of study:
Experimental_studies
/
Rct
Language:
English
Year:
2020
Document type:
Preprint