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

Chuming Chen | Zendy; Karen Ross | Zendy; Sachin Gavali | Zendy; Julie Cowart | Zendy; Cathy Wu | Zendy

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

Author(s) -

Chuming Chen,

Karen Ross,

Sachin Gavali,

Julie Cowart,

Cathy Wu

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab694

Subject(s) - sparql , computer science , rdf , download , world wide web , license , covid-19 , graph database , knowledge graph , graph , semantic web , information retrieval , database , infectious disease (medical specialty) , medicine , disease , pathology , theoretical computer science , operating system

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.

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