An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19

被引：0

作者：

Qiao, Yinbo ^{[1
]}

Yang, Zhihao ^{[1
]}

Lin, Hongfei ^{[1
]}

Wang, Jian ^{[1
]}

机构：

[1] Dalian Univ Technol, Dalian 116024, Peoples R China

来源：

HEALTH INFORMATION PROCESSING, CHIP 2022 | 2023年 / 1772卷

关键词：

COVID-19; Knowledge graph; Knowledge graph embedding;

D O I：

10.1007/978-981-19-9865-2_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Question Answering based on Knowledge Graph (KG) has emerged as a popular research area in general domain. However, few works focus on the COVID-19 kg-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at https:// github.com/CHNcreater/COVID-19-KGQA.

引用

页码：156 / 169

页数：14

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