摘要
知识图谱技术在从关系的角度发掘实体与实体间关联方面具有独特优势,中医药知识的高度复杂性和内在强关联性的特点使知识图谱技术更适合应用于中医药学研究.中医药知识图谱研究在模式层构建、实体命名和识别等方面取得了长足发展,深度学习、Python编程、链路预测、预训练模型等技术被引入到知识图谱中,进一步提高了知识图谱的可靠性和构建效率.回顾了中医药知识图谱技术近年来的发展历程,从概念、构建方法、在中医药领域的应用及不足几个方面进行综述,以期促进知识图谱技术在中医药领域的发展和应用.
Abstract
Knowledge graph technology has unique advantages in exploring the relationships between entities from the perspective of relationships.The high complexity and strong inherent correlation of traditional Chinese medicine(TCM)knowledge make it more suitable for application as a favorable tool in TCM research and exploring the relationships between complex objects.The research on TCM knowledge graph has made significant progress in pattern layer construction,entity naming,and recognition.Technologies such as deep learning,Python programming,link prediction,and pre-trained models have been introduced into the knowledge graph,further improving the reliability and construction efficiency of the knowledge graph.This article reviews the development process of TCM knowledge graph technology in recent years,and provides a literature review from the concepts,construction methods,applications in the field of TCM,and shortcomings,so as to promote the development and application of knowledge graph in the field of TCM.
基金项目
中国医学科学院医学与健康科技创新项目(2022-I2M-1-018)
国家科技期刊平台
NSTL
万方数据