[2203.09975] BIOS: An Algorithmically Generated Biomedical Knowledge Graph
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Computer Science > Computation and Language

arXiv:2203.09975 (cs)
[Submitted on 18 Mar 2022 (v1), last revised 24 Apr 2022 (this version, v2)]

Title:BIOS: An Algorithmically Generated Biomedical Knowledge Graph

Authors:Sheng Yu, Zheng Yuan, Jun Xia, Shengxuan Luo, Huaiyuan Ying, Sihang Zeng, Jingyi Ren, Hongyi Yuan, Zhengyun Zhao, Yucong Lin, Keming Lu, Jing Wang, Yutao Xie, Heung-Yeung Shum
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Abstract: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 decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with 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 generated completely 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 present the methodology for developing BIOS, including the curation of raw biomedical terms, computational identification of synonymous terms and aggregation of these terms to create concept nodes, semantic type classification of the concepts, relation identification, and biomedical machine translation. We provide statistics on the current BIOS content and perform preliminary assessments of term quality, synonym grouping, and relation extraction. The results suggest that machine learning-based BioMedKG development is a viable alternative to traditional expert curation.
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2203.09975 [cs.CL]
  (or arXiv:2203.09975v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2203.09975

Submission history

From: Sheng Yu [view email]
[v1] Fri, 18 Mar 2022 14:09:22 UTC (1,380 KB)
[v2] Sun, 24 Apr 2022 10:11:59 UTC (1,625 KB)
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