Building a PubMed knowledge graph

• Published in: Scientific data Vol. 7; no. 1; p. 205
• Main Authors: Xu, Jian, Kim, Sunkyu, Song, Min, Jeong, Minbyul, Kim, Donghyeon, Kang, Jaewoo, Rousseau, Justin F, Li, Xin, Xu, Weijia, Torvik, Vetle I, Bu, Yi, Chen, Chongyan, Ebeid, Islam Akef, Li, Daifeng, Ding, Ying
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 26.06.2020; Nature Publishing Group UK
• Subjects: Authorship; Computer applications; Data Descriptor; Deep Learning; Funding; Knowledge Bases; Learning algorithms; Machine learning; PubMed
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-0543-2

PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID , and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.