Abstract
Traditional Chinese medicine (TCM) is a bright pearl in the treasure house of healthcare applications that has attracted increasing attention due to its huge applying potential, especially in the prevention and intervention of COVID-19 Pandemic. Such applications for healthcare decision-making are powerful tools to help provide actionable and explainable medical services to patients, but they are a knowledge-driven system and rely on knowledge graphs. However, most of TCM-related materials and guidebooks are preserved in the form of documents, lacking structured information and conceptual knowledge. To facility the study of domain-specific knowledge graphs in TCM, we define the ontology of knowledge graph in TCM with 29 types of entities and 32 types of relations, and then annotate a high-quality dataset (TCM-ERE) for Entity and Relation Extraction (E &RE) aligning with the concepts of the TCM-ontology. More than 40% of relations can only be inferred from multiple sentences in TCM-ERE, thus it can also be used for Chinese document-level E &RE research. The baseline models trained on the TCM-ERE are used to extract fact triples from TCM medical records for the enriching scale of the TCM-related knowledge graph (TCM-KG). TCM-ERE, TCM-KG and the baseline models are publicly available at https://gitee.com/yi_zhi_wei/acup1.git.
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Acknowledgment
This work is partially supported by the National Key R &D Program of China (Grant 2022YFF0903100), the Key Research and Development Program of Shandong Province (Grant 2020CXGC010903), the National Natural Science Foundation of China (Grant 62073103), the Leading Benefiting People Fund Of Qingdao Science and Technology (21-1-4-rkjk-16-nsh), and the Mount Taishan Scholar Project Special Fund.
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Yi, Z. et al. (2023). Knowledge Graph Construction for Healthcare Services in Traditional Chinese Medicine. In: Wang, Z., Wang, S., Xu, H. (eds) Service Science. ICSS 2023. Communications in Computer and Information Science, vol 1844. Springer, Singapore. https://doi.org/10.1007/978-981-99-4402-6_23
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DOI: https://doi.org/10.1007/978-981-99-4402-6_23
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