Entity extraction and graph construction based on Chinese medical text

YANG Ye; PEI Lei; HOU Fengzhen

doi:10.11665/j.issn.1000-5048.2023030903

Journal of China Pharmaceutical University > 2023 > 54(3): 363-371. > DOI: 10.11665/j.issn.1000-5048.2023030903

YANG Ye, PEI Lei, HOU Fengzhen. Entity extraction and graph construction based on Chinese medical text[J]. Journal of China Pharmaceutical University, 2023, 54(3): 363-371. DOI: 10.11665/j.issn.1000-5048.2023030903

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Entity extraction and graph construction based on Chinese medical text

Institute of Medical Big Data and Artificial Intelligence, School of Science, China Pharmaceutical University, Nanjing 211198, China

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Received Date: March 08, 2023
Revised Date: June 11, 2023

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Abstract

Knowledge graph technology has promoted the progress of new drug research and development, but domestic research starts late and domain knowledge is mostly stored in text, resulting in low rate of knowledge graph reuse.Based on multi-source and heterogeneous medical texts, this paper designed a Chinese named entity recognition model based on Bert-wwm-ext pre-training model and also integrated cascade thought, which reduced the complexity of traditional single classification and further improved the efficiency of text recognition.The experimental results showed that the model achieved the best performance with an F1-score of 0.903, a precision of 89.2%, and a recall rate of 91.5% on the self-built dataset.At the same time, the model was applied to the public dataset CCKS2019, and the results showed that the model had better performance and recognition effect.Using this model, this paper constructed a Chinese medical knowledge graph, involving 13 530 entities, 10 939 attributes and 39 247 relationships of them in total.The Chinese medical entity extraction and graph construction method proposed in this paper is expected to help researchers accelerate the new discovery of medical knowledge, and shorten the process of new drug discovery.
- Chinese medical text,
- named entity recognition model,
- Bert-wwm-ext pre-training model,
- cascade thought,
- knowledge graph

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