医学知识图谱构建关键技术及研究进展

谭玲; 鄂海红; 匡泽民; 宋美娜; 刘毓; 陈正宇; 谢晓璇; 李峻迪; 范家伟; 王晴川; 康霄阳

doi:10.11959/issn.2096-0271.2021040

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医学知识图谱构建关键技术及研究进展

研究 | 更新时间：2024-06-03

- 医学知识图谱构建关键技术及研究进展
- Key technologies and research progress of medical knowledge graph construction
- 大数据 2021年7卷第4期页码：2021040
- 作者机构：
  
  1. 北京邮电大学，北京 100876
  2. 首都医科大学附属北京安贞医院，北京 100029
- 作者简介：
  
  [ "谭玲（1993-），女，北京邮电大学博士生，主要研究方向为知识图谱及自然语言处理、大数据及人工智能" ]
  [ "鄂海红（1982-），女，博士，北京邮电大学副教授，主要研究方向为大数据及人工智能、知识图谱及自然语言处理、大数据中台、分布式微服务架构" ]
  [ "匡泽民（1979-），男，博士，首都医科大学附属北京安贞医院高血压科主任医师，主要研究方向为高血压精准诊断与治疗、心血管临床药理、医学人工智能" ]
  [ "宋美娜（1974-），女，博士，北京邮电大学教授，主要研究方向为大数据、联邦学习及医疗健康、金融科技应用、大数据、联邦学习及医疗健康" ]
  [ "刘毓（1998-），女，北京邮电大学硕士生，主要研究方向为知识图谱" ]
  [ "陈正宇（1997-），男，北京邮电大学硕士生，主要研究方向为计算机视觉、知识图谱" ]
  [ "谢晓璇（1997-），女，北京邮电大学硕士生，主要研究方向为知识图谱" ]
  [ "李峻迪（1997-），男，北京邮电大学硕士生，主要研究方向为智能对话系统和Java开发" ]
  [ "范家伟（1998-），男，北京邮电大学硕士生，主要研究方向为深度学习" ]
  [ "王晴川（1997-），女，北京邮电大学硕士生，主要研究方向为自然语言处理" ]
  [ "康霄阳（1997-），男，北京邮电大学硕士生，主要研究方向为机器学习、计算机视觉" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61902034);教育部信息网络工程研究中心资助项目;Engineering Research Center of Information Networks, Ministry of Education
- DOI：10.11959/issn.2096-0271.2021040
  中图分类号： TP3
- 网络首发：2021-07，
  
  纸质出版：2021-07-15
- 稿件说明：
移动端阅览
谭玲, 鄂海红, 匡泽民, 等. 医学知识图谱构建关键技术及研究进展[J]. 大数据, 2021,7(4):2021040.

Ling TAN, Haihong E, Zemin KUANG, et al. Key technologies and research progress of medical knowledge graph construction[J]. Big data research, 2021, 7(4): 2021040.
谭玲, 鄂海红, 匡泽民, 等. 医学知识图谱构建关键技术及研究进展[J]. 大数据, 2021,7(4):2021040. DOI： 10.11959/issn.2096-0271.2021040.

Ling TAN, Haihong E, Zemin KUANG, et al. Key technologies and research progress of medical knowledge graph construction[J]. Big data research, 2021, 7(4): 2021040. DOI： 10.11959/issn.2096-0271.2021040.

摘要

随着互联网技术的不断迭代更新，对海量数据的语义理解变得越来越重要。知识图谱是一种揭示实体之间关系的语义网络，医学是知识图谱应用较广的垂直领域之一，医学知识图谱的构建也是目前国内外人工智能领域研究的热点。从医学知识图谱本体构建出发，依次对命名实体识别、实体关系抽取、实体对齐、实体链接、知识图谱存储、知识图谱应用进行综述，详细介绍了近年来医学知识图谱构建过程中涉及的难点、现有技术、挑战及未来研究方向，并介绍了医学知识图谱应用，最后对未来发展方向进行了展望。

Abstract

With the continuous iterative updating of Internet technology

the semantic understanding of massive data is becoming more and more important.Knowledge graph is a kind of semantic network that reveals the relationship between entities.Medicine is one of the most widely used vertical fields of knowledge graph.The construction of medical knowledge graph is also a hot research in the field of artificial intelligence at home and abroad.Starting from the ontology construction of medical knowledge graph

named entity recognition

entity relationship extraction

entity alignment

entity linking

knowledge graph storage and application of knowledge graph were reviewed.The difficulties

existing technologies

challenges and future research directions in the process of constructing medical knowledge graph in recent years were introduced.Finally

the application of knowledge graph and the future development direction of medical knowledge graph were discussed.

关键词

Keywords

references

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