Meta-path reasoning of knowledge graph for commonsense question answering

Abstract

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.

Graphical abstract

Keywords

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

Miao Zhang received his MSc degree in computer technology from Central China Normal University, China in 2018. He is currently working toward to the PhD degree in National Engineering Research Center for E-Learning, Central China Normal University, China. His research interests include QA system and knowledge graph

Tingting He received the BE degree and in Software Engineering from Wuhan University, China in 1985, the MSc degree in Applied Software and Theory of Computer from Wuhan University, China in 1988 and the PhD degree in applied linguistics from Central China Normal University, China in 2003. She is a Full Professor in School of Computer, Central China Normal University, China. Her areas of research interests are natural language processing, computational intelligence and deep learning

Ming Dong received his PhD degree from Huazhong University of Science and Technology (HUST), China in 2021. He received his bachelor degree in Information Management and Information System from Zhongnan University of Economics and Law (ZUEL), China in 2015. He is a lecturer in Central China Normal University (CCNU), China. His research interests include social networks, rumor detection, and natural language processing

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Acknowledgements

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).

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