Meta-path reasoning of knowledge graph for commonsense question answering

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成果类型：

期刊论文

作者：

Zhang, Miao;He, Tingting*;Dong, Ming

通讯作者：

He, Tingting;Dong, M

作者机构：

[Zhang, Miao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.

[Zhang, Miao; He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.

[Zhang, Miao; He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.

[He, Tingting; Dong, Ming; Dong, M] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.

通讯机构：

[He, TT; Dong, M ] C

Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.

Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.

Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.

语种：

英文

关键词：

question answering;knowledge graph;graph neural network;meta-path reasoning

期刊：

计算机科学前沿(英文)

ISSN：

2095-2228

年：

2024

卷：

期：

页码：

181303-null

DOI：

10.1007/s11704-022-2336-6

基金类别：

This work was partially supported by the Key Research and Development Program of Hubei Province (2020BAB017), and the Scientific Research Center Program of National Language Commission (ZDI135-135), and the Fundamental Research Funds for the Central Universities (KJ02502022-0155, CCNU22XJ037).

机构署名：

本校为第一且通讯机构

院系归属：

计算机学院

国家数字化学习工程技术研究中心

摘要：

Commonsense question answering (CQA) requires understanding and reasoning over QA context and related commonsense knowledge, such as a structured Knowledge Graph (KG). Existing studies combine language models and graph neural networks to model inference. However, traditional knowledge graph are mostly concept-based, ignoring direct path evidence necessary for accurate reasoning. In this paper, we propose MRGNN (Meta-path Reasoning Graph Neural Network), a novel model that comprehensively captures sequential semantic information from concepts and paths. In MRGNN, meta-paths are introduced as di...

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Meta-path reasoning of knowledge graph for commonsense question answering

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