Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention. The models developed based on Graph Inductive Learning, called GraIL-based models, have shown promising potential for this task. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs. Besides, the enclosing subgraph extraction in most GraIL-based models restricts the model from extracting enough discriminative information for reasoning. Consequently, the expressive ability of these models is limited. To address the problems, we propose a novel GraIL-based inductive relation reasoning model, termed MINES, by introducing a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Concretely, the message intercommunication mechanism is designed to capture the omitted hidden mutual information. It introduces bi-directed information interactions between connected entities by inserting an undirected/bi-directed GCN layer between uni-directed RGCN layers. Moreover, inspired by the success of involving more neighbors in other graph-based tasks, we extend the neighborhood area beyond the enclosing subgraph to enhance the information collection for inductive relation reasoning. Extensive experiments on twelve inductive benchmark datasets demonstrate that our MINES outperforms existing state-of-the-art models, and show the effectiveness of our intercommunication mechanism and reasoning on the neighbor-enhanced subgraph.

翻译：知识图谱的归纳关系推理旨在推断全新实体之间的缺失链接，正日益受到关注。基于图归纳学习开发的模型（称为GraIL类模型）在该任务上展现出巨大潜力。然而，单向消息传递机制阻碍了此类模型挖掘有向图中实体间隐藏的相互关联。此外，多数GraIL类模型采用的封闭子图提取方式限制了模型获取足够判别性信息进行推理的能力，进而削弱了模型的表达能力。为解决上述问题，我们提出一种新型GraIL类归纳关系推理模型MINES，通过在邻域增强子图中引入消息交互机制实现。具体而言，消息交互机制通过在有向RGCN层之间插入无向/双向GCN层，在相连实体间引入双向信息交互，从而捕获被忽略的隐藏互信息。同时受其他图任务中扩展邻域成功的启发，我们将邻域范围扩展至封闭子图之外，以增强归纳关系推理的信息收集能力。在十二个归纳基准数据集上的大量实验表明，MINES模型优于现有最先进模型，同时验证了所提交互机制及基于邻域增强子图推理的有效性。