Chinese Medical Sciences Journal ›› 2009, Vol. 34 ›› Issue (4): 277-280.doi: 10.24920/003701

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本体:强人工智能的基石

杨啸林1,*(),王哲1,潘虹洁1,朱彦2   

  1. 1.中国医学科学院基础医学研究所生物医学工程系,北京,100730 中国
    2. 中国中医科学院中医药信息研究所知识组织与标准化研究部,北京,100700中国
  • 收稿日期:2019-12-13 接受日期:2019-12-23 出版日期:2009-10-27 发布日期:2020-01-14
  • 通讯作者: 杨啸林 E-mail:yangxl@pumc.edu.cn

Ontology: Footstone for Strong Artificial Intelligence

Yang Xiaolin1,*(),Wang Zhe1,Pan Hongjie1,Zhu Yan2   

  1. 1.Department of Biomedical Engineering, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, 100730 China
    2.Research Division of Knowledge Organization and Standardization, Institute of Information on Traditional Chinese Medicine, Chinese Academy of Chinese Medical Sciences, Beijing, 100700 China
  • Received:2019-12-13 Accepted:2019-12-23 Online:2009-10-27 Published:2020-01-14
  • Contact: Yang Xiaolin E-mail:yangxl@pumc.edu.cn

摘要:

最近十多年来,人工智能在生物医学领域的应用迅速增长,这源于生物医学领域数据的快速积累,计算机计算能力的提升,以及深度学习方法的不断发展。但是,现阶段人工智能在处理复杂综合的医学问题上和人相比仍有很大距离。如何让计算机拥有更强的智能,在医学领域有更广泛的应用,本体在这个过程中能够发挥重要作用。利用本体可以实现数据和元数据的标准化;本体内的语义关系可以丰富数据分析的方法并提升机器学习方法的性能;本体支持语义水平的数据整合,从而帮助计算机实现自然语言的逻辑表示,提升数据的机器可读性。本体是机器学习方法走向强人工智能重要一步。基于此背景,2019中国生物医学本体和术语研讨会于2019年秋在北京举行。会议以“标准化,让机器理解你的数据”为主题。此次会议的成功举办对中国生物医学领域本体的发展起到推动作用,并将促进中国本体研究和应用与国际标准及FAIR准则(FAIR Data Principle)的接轨。

关键词: 本体, 人工智能, 生物医学, 大数据

Abstract:

In the past ten years, the application of artificial intelligence (AI) in biomedicine has increased rapidly, which roots in the rapid growth of biomedicine data, the improvement of computing performance, and the development of deep learning methods. At present, there are great difficulties in front of AI for solving complex and comprehensive medical problems. Ontology can play an important role in how to make machines have stronger intelligence and has wider applications in the medical field. By using ontologies, (meta) data can be standardized so that data quality is improved and more data analysis methods can be introduced, data integration can be supported by the semantics relationships which are specified in ontologies, and effective logic expression in nature language can be better understood by machine. This can be a pathway to stronger AI. Under this circumstance, the Chinese Conference on Biomedical Ontology and Terminology was held in Beijing in autumn 2019, with the theme “Making Machine Understand Data”. The success of this conference further improves the development of ontology in the field of biomedical information in China, and will promote the integration of Chinese ontology research and application with the international standards and the findability, accessibility, interoperability, and reusability(FAIR) Data Principle.

Key words: ontology, artificial intelligence, biomedicine, big data

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