首页|Meta-path reasoning of knowledge graph for commonsense question answering
Meta-path reasoning of knowledge graph for commonsense question answering
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
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 direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously.We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets,showing the effectiveness of MRGNN.Also,we conduct further ablation experiments and explain the reasoning behavior through the case study.
National Engineering Research Center for E-Learning,Central China Normal University,Wuhan 430079,China
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Central China Normal University,Wuhan 430079,China
National Language Resources Monitoring & Research Center for Network Media,Central China Normal University,Wuhan 430079,China
School of Computer,Central China Normal University,Wuhan 430079,China
展开 >
Key Research and Development Program of Hubei ProvinceScientific Research Center Program of National Language CommissionFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities
NETL
NSTL
万方数据
