Re-curation and rational enrichment of knowledge graphs in Biological Expression Language

Database (Oxford). 2019 Jan 1;2019:baz068. doi: 10.1093/database/baz068.


The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution.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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Mining*
  • Pattern Recognition, Automated*
  • Semantics*