Chinese Medical Sciences Journal ›› 2019, Vol. 34 ›› Issue (4): 277-280.doi: 10.24920/003701

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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 Published:2019-12-31
  • Contact: Yang Xiaolin

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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