COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases
Date
2021-10-06
Journal Title
Journal ISSN
Volume Title
Publisher
Bioinformatics
Abstract
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.
Description
This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Chuming Chen, Karen E Ross, Sachin Gavali, Julie E Cowart, Cathy H Wu, COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases, Bioinformatics, Volume 37, Issue 23, 1 December 2021, Pages 4597–4598, https://doi.org/10.1093/bioinformatics/btab694 is available online at: https://doi.org/10.1093/bioinformatics/btab694.
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Citation
Chuming Chen, Karen E Ross, Sachin Gavali, Julie E Cowart, Cathy H Wu, COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases, Bioinformatics, Volume 37, Issue 23, 1 December 2021, Pages 4597–4598, https://doi.org/10.1093/bioinformatics/btab694