Extracting relations from traditional Chinese medicine literature via heterogeneous entity networks | Zendy

Huaiyu Wan | Zendy; Marie-Francine Moens | Zendy; Walter Luyten | Zendy; Xuezhong Zhou | Zendy; Qiaozhu Mei | Zendy; Lu Liu | Zendy; Jie Tang | Zendy

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Extracting relations from traditional Chinese medicine literature via heterogeneous entity networks

Author(s) -

Huaiyu Wan,

Marie-Francine Moens,

Walter Luyten,

Xuezhong Zhou,

Qiaozhu Mei,

Lu Liu,

Jie Tang

Publication year - 2015

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocv092

Subject(s) - computer science , support vector machine , artificial intelligence , classifier (uml) , sentence , traditional chinese medicine , inference , exploit , relationship extraction , machine learning , natural language processing , data mining , medicine , information extraction , alternative medicine , computer security , pathology

Traditional Chinese medicine (TCM) is a unique and complex medical system that has developed over thousands of years. This article studies the problem of automatically extracting meaningful relations of entities from TCM literature, for the purposes of assisting clinical treatment or poly-pharmacology research and promoting the understanding of TCM in Western countries.

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