  PubMed is an essential resource for the medical domain, but useful concepts
are either difficult to extract or are ambiguated, 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 the 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 a 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. The PKG is freely
available on Figshare (https://figshare.com/s/6327a55355fc2c99f3a2, simplified
version that exclude PubMed raw data) and TACC website
(http://er.tacc.utexas.edu/datasets/ped, full version).
