Kavli Affiliate: Jing Wang
| First 5 Authors: Sheng Yu, Zheng Yuan, Jun Xia, Shengxuan Luo, Huaiyuan Ying
| Summary:
Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for
biomedical and healthcare big data and artificial intelligence (AI),
facilitating natural language processing, model development, and data exchange.
For many decades, these knowledge graphs have been built via expert curation,
which can no longer catch up with the speed of today’s AI development, and a
transition to algorithmically generated BioMedKGs is necessary. In this work,
we introduce the Biomedical Informatics Ontology System (BIOS), the first large
scale publicly available BioMedKG that is fully generated by machine learning
algorithms. BIOS currently contains 4.1 million concepts, 7.4 million terms in
two languages, and 7.3 million relation triplets. We introduce the methodology
for developing BIOS, which covers curation of raw biomedical terms,
computationally identifying synonymous terms and aggregating them to create
concept nodes, semantic type classification of the concepts, relation
identification, and biomedical machine translation. We provide statistics about
the current content of BIOS and perform preliminary assessment for term
quality, synonym grouping, and relation extraction. Results suggest that
machine learning-based BioMedKG development is a totally viable solution for
replacing traditional expert curation.
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