Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text

Authors:
Cungen Cao;Haitao Wang;Yuefei Sui
Affiliations:
Knowledge Acquisition and Sharing Group, Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Knowledge Acquisition and Sharing Group, Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Knowledge Acquisition and Sharing Group, Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Venue:
Artificial Intelligence in Medicine
Year:
2004

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Abstract

Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting knowledge of drugs and formulae from semi-structured text. The system consists of two components: an executable knowledge extraction language (or EKEL) for specifying knowledge-extracting agents, and a support machine for executing EKEL programs. Experiments show that the system is adequate of extracting knowledge of herbal drugs and formulae from semi-structured text.