COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases

25Citations
Citations of this article
26Readers
Mendeley users who have this article in their library.

Abstract

Summary: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download.

Cite

CITATION STYLE

APA

Chen, C., Ross, K. E., Gavali, S., Cowart, J. E., & Wu, C. H. (2021). COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases. Bioinformatics, 37(23), 4597–4598. https://doi.org/10.1093/bioinformatics/btab694

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free