BIOS: An Algorithmically Generated Biomedical Knowledge Graph

• URL: http://arxiv.org/abs/2203.09975v1
• Date: Fri, 18 Mar 2022 14:09:22 GMT
• 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
• Abstract summary: We introduce the Biomedical Informatics Ontology System (BIOS), the first large scale publicly available BioMedKG that is fully generated by machine learning algorithms. BIOS contains 4.1 million concepts, 7.4 million terms in two languages, and 7.3 million relation triplets. Results suggest that machine learning-based BioMedKG development is a totally viable solution for replacing traditional expert curation.
• Score: 4.030892610300306
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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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