Paper Info
Title
Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language
Abstract
The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment. Database URL: https://github. com/bel-enrichment/results
Year
DOI
Venue
2019
10.1093/database/baz068
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Keywords
Field
DocType
Natural language processing,Information extraction,Biocuration,Biological Expression Language,Knowledge graphs
Information density,Knowledge graph,Information retrieval,Biology,Bioinformatics,MEDLINE,Syntax,Workflow
Journal
Volume
ISSN
Citations 
2019
1758-0463
0
PageRank 
References 
Authors
0.34
14
11
Name
Order
Citations
PageRank
Charles Hoyt100.34
Daniel Domingo-Fernández295.48
Rana Aldisi300.34
Lingling Xu4408.56
Kristian Kolpeja500.34
Sandra Spalek600.34
Esther Wollert700.34
John A. Bachman821.39
Benjamin M. Gyori963.60
Patrick Greene1000.34
Martin Hofmann-Apitius1137230.08