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    <abstract>Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing knowledge in relational digraphs and achieves notable results. However, during reasoning with PRGNN, two important properties are often overlooked: (1) the sequentiality of relation composition, where the order of combining different relations affects the semantics of the relational rules, and (2) the lagged entity information propagation, where the transmission speed of required information lags behind the appearance speed of new entities. Ignoring these properties leads to incorrect relational rule learning and decreased reasoning accuracy. To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN). Specifically, RUN-GNN employs a query related fusion gate unit to model the sequentiality of relation composition and utilizes a buffering update mechanism to alleviate the negative effect of lagged entity information propagation, resulting in higher-quality relational rule learning. Experimental results on multiple datasets demonstrate the superiority of RUN-GNN is superior on both transductive and inductive link prediction tasks.</abstract>
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%0 Conference Proceedings
%T Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
%A Wu, Shuhan
%A Wan, Huaiyu
%A Chen, Wei
%A Wu, Yuting
%A Shen, Junfeng
%A Lin, Youfang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wu-etal-2023-towards-enhancing
%X Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing knowledge in relational digraphs and achieves notable results. However, during reasoning with PRGNN, two important properties are often overlooked: (1) the sequentiality of relation composition, where the order of combining different relations affects the semantics of the relational rules, and (2) the lagged entity information propagation, where the transmission speed of required information lags behind the appearance speed of new entities. Ignoring these properties leads to incorrect relational rule learning and decreased reasoning accuracy. To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN). Specifically, RUN-GNN employs a query related fusion gate unit to model the sequentiality of relation composition and utilizes a buffering update mechanism to alleviate the negative effect of lagged entity information propagation, resulting in higher-quality relational rule learning. Experimental results on multiple datasets demonstrate the superiority of RUN-GNN is superior on both transductive and inductive link prediction tasks.
%R 10.18653/v1/2023.findings-emnlp.676
%U https://aclanthology.org/2023.findings-emnlp.676
%U https://doi.org/10.18653/v1/2023.findings-emnlp.676
%P 10082-10097

Markdown (Informal)

[Towards Enhancing Relational Rules for Knowledge Graph Link Prediction](https://aclanthology.org/2023.findings-emnlp.676) (Wu et al., Findings 2023)

• Towards Enhancing Relational Rules for Knowledge Graph Link Prediction (Wu et al., Findings 2023)

ACL

• Shuhan Wu, Huaiyu Wan, Wei Chen, Yuting Wu, Junfeng Shen, and Youfang Lin. 2023. Towards Enhancing Relational Rules for Knowledge Graph Link Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10082–10097, Singapore. Association for Computational Linguistics.