Multi-Hop Reasoning for Question Answering with Knowledge Graph

被引：2

作者：

Zhang, Jiayuan ^{[1
,2
]}

Cai, Yifei ^{[1
,2
]}

Zhang, Qian ^{[1
,2
]}

Cao, Zehao ^{[1
,2
]}

Cheng, Zhenrong ^{[1
,2
]}

Li, Dongmei ^{[1
,2
]}

Meng, Xianghao ^{[1
,2
]}

机构：

[1] Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing, Peoples R China

[2] Natl Forestry & Grassland Adm, Engn Res Ctr Forestry Oriented Intelligent Inform, Beijing, Peoples R China

来源：

2021 IEEE/ACIS 20TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS 2021-SUMMER) | 2021年

关键词：

Knowledge Graph; Question Answering System; CNN;

D O I：

10.1109/ICIS51600.2021.9516865

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multi-hop Question Answering over Knowledge Graphs (KGQA) in previous studies has achieved remarkable results by exploiting the prediction property of Knowledge Graphs (KG) embedding. However, when facing Chinese sentences, its answer selection performs poorly. We improve the method for KGQA by combining the traditional method for KGQA with a lattice based CNN (LCN) model. We refine the granularity of questions and answers to make its coverage more extensive and generalizable, and expand the answer set to improve the performance in single results.

引用

页码：121 / 125

页数：5

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