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    <abstract>In knowledge graphs (KGs), predicting missing relations is a critical reasoning task. Recent subgraph-based models have delved into inductive settings, which aim to predict relations between newly added entities. While these models have demonstrated the ability for inductive reasoning, they only consider the structural information of the subgraph and neglect the loss of semantic information caused by replacing entities with nodes. To address this problem, we propose a novel Commonsense Subgraph Meta-Learning (CSML) model. Specifically, we extract concepts from entities, which can be viewed as high-level semantic information. Unlike previous methods, we use concepts instead of nodes to construct commonsense subgraphs. By combining these with structural subgraphs, we can leverage both structural and semantic information for more comprehensive and rational predictions. Furthermore, we regard concepts as meta-information and employ meta-learning to facilitate rapid knowledge transfer, thus addressing more complex few-shot scenarios. Experimental results confirm the superior performance of our model in both standard and few-shot inductive reasoning.</abstract>
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%0 Conference Proceedings
%T Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning
%A Zhao, Feng
%A Zhang, Zhilu
%A Yan, Cheng
%A Liu, Xianggan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhao-etal-2025-commonsense
%X In knowledge graphs (KGs), predicting missing relations is a critical reasoning task. Recent subgraph-based models have delved into inductive settings, which aim to predict relations between newly added entities. While these models have demonstrated the ability for inductive reasoning, they only consider the structural information of the subgraph and neglect the loss of semantic information caused by replacing entities with nodes. To address this problem, we propose a novel Commonsense Subgraph Meta-Learning (CSML) model. Specifically, we extract concepts from entities, which can be viewed as high-level semantic information. Unlike previous methods, we use concepts instead of nodes to construct commonsense subgraphs. By combining these with structural subgraphs, we can leverage both structural and semantic information for more comprehensive and rational predictions. Furthermore, we regard concepts as meta-information and employ meta-learning to facilitate rapid knowledge transfer, thus addressing more complex few-shot scenarios. Experimental results confirm the superior performance of our model in both standard and few-shot inductive reasoning.
%U https://aclanthology.org/2025.coling-main.150/
%P 2198-2206

Markdown (Informal)

[Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning](https://aclanthology.org/2025.coling-main.150/) (Zhao et al., COLING 2025)

• Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning (Zhao et al., COLING 2025)

ACL

• Feng Zhao, Zhilu Zhang, Cheng Yan, and Xianggan Liu. 2025. Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning. In Proceedings of the 31st International Conference on Computational Linguistics, pages 2198–2206, Abu Dhabi, UAE. Association for Computational Linguistics.