Question-Answering system based on the Knowledge Graph of Traditional Chinese Medicine

被引：1

作者：

Miao, Fang ^{[1
]}

Wang, XueTing ^{[1
]}

Zhang, Pu ^{[1
]}

Jin, Libiao ^{[1
]}

机构：

[1] Commun Univ China, Sch Informat Engn, Beijing, Peoples R China

来源：

2019 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2019), VOL 2 | 2019年

基金：

国家重点研发计划;

关键词：

Knowledge Graph; question-answering system; Chinese medicine; question analysis; natural language processing;

D O I：

10.1109/IHMSC.2019.10156

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the development of artificial intelligence, the emergence of the QA system meets the search needs of people in the mass information age. The traditional question-answering system mostly matches the questions with fixed templates, and the dataset of questions and answers often rely on human-designed features, which is time-consuming and with low accuracy. To address this dilemma, the current prevailing technology of Knowledge Graph provides a new way, helping to build a domain-specific intelligent question answering system. In this paper, we combine the Knowledge Graph with the question and answer system to analyze the relationship of diseases, prescriptions, and Chinese herbal medicines, and finally implement a smart question answering system for the Traditional Chinese Medicine.

引用

页码：264 / 267

页数：4

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