About: Building a PubMed knowledge graph

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Building a PubMed knowledge graph
Creator	Li, Xin Xu, Weijia Xu, Jian Song, Min Bu, Yi Chen, Chongyan Ding, Ying Jeong, Minbyul Kang, Jaewoo Kim, Donghyeon Kim, Sunkyu Li, Daifeng Rousseau, Justin Torvik, Vetle Akef Ebeid, Islam
Source	Medline; PMC
abstract	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.
has issue date	2020-06-26(xsd:dateTime)
bibo:doi	10.1038/s41597-020-0543-2
bibo:pmid	32591513
has license	cc-by
sha1sum (hex)	9a06828f8dd0f353cd95b933aa8ea422ff269eec
schema:url	https://doi.org/10.1038/s41597-020-0543-2
resource representing a document's title	Building a PubMed knowledge graph
has PubMed Central identifier	PMC7320186
has PubMed identifier	32591513
schema:publication	Sci Data
resource representing a document's body	covid:9a06828f8dd0f353cd95b933aa8ea422ff269eec#body_text
is schema:about of	named entity 'ADDRESS' named entity 'TO MEASURE' named entity 'METHOD' named entity 'USEFUL' named entity 'MULTI-SOURCE' named entity 'EXPORTER' named entity 'ART' named entity 'background' named entity 'PKG' named entity 'AND' named entity 'connections' named entity 'extraction' named entity 'enabling' named entity 'affiliation' named entity 'NIH' named entity 'author' named entity 'connections' named entity 'based' named entity 'NIH' named entity 'PubMed' named entity 'F1 score' named entity 'knowledge graph' named entity 'mutation' named entity 'normalization model' named entity 'Figshare' named entity 'Chan Kwok Hung' named entity 'information integration' named entity 'decision rules' named entity 'entity extraction' named entity 'data format' named entity 'NIH' named entity 'Medline' named entity 'formal employment' named entity 'nature.com' named entity 'FDA' named entity 'triptans' named entity 'email' named entity 'PubMed' named entity 'NCBI' named entity 'Yuen Kwok-Yung' named entity 'NIH' named entity 'triptans' named entity 'AHRQ' named entity 'PubMed' named entity 'Parser' named entity 'entity extraction' named entity 'ResearcherID' named entity 'open-access' named entity 'Semantic Scholar' named entity 'PMIDs' named entity 'proper nouns' named entity 'curricula vitae' named entity 'meta-data' named entity 'Semantic Scholar' named entity 'ORCID' named entity 'strings' named entity 'ZIP code' named entity 'PubMed' named entity 'PubMed' named entity 'PubMed' named entity 'PubMed' named entity 'ORCID' named entity 'Google Scholar' named entity 'Semantic Scholar' named entity 'CDC' named entity 'NIH' named entity 'nature.com'

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