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.

翻译：图神经网络（GNNs）在知识图谱推理中展现出良好性能。一种名为渐进式关系图神经网络（PRGNN）的GNN变体利用关系规则推断关系有向图中的缺失知识，取得了显著成果。然而，在使用PRGNN进行推理时，两个关键特性常被忽视：（1）关系组合的顺序性，即不同关系组合的顺序会影响关系规则的语义；（2）实体信息传播的滞后性，即所需信息的传输速度滞后于新实体的出现速度。忽视这些特性会导致关系规则学习错误及推理精度下降。针对上述问题，本文提出一种新型知识图谱推理方法——关系规则增强图神经网络（RUN-GNN）。具体而言，RUN-GNN采用查询相关融合门控单元建模关系组合的顺序性，并利用缓冲更新机制缓解实体信息传播滞后的负面影响，从而获得更高质量的关系规则学习效果。在多个数据集上的实验结果表明，RUN-GNN在归纳式与直推式链接预测任务中均展现出优越性能。