Abstract
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.
Abstract (translated)
知识图谱中的基于图归纳学习的关联推理任务，旨在推断新实体之间的缺失链接，已经引起了越来越多的关注。基于图归纳学习的开发模型，称为GraIL-based模型，在这些任务中表现出了很好的潜力。然而，单向的消息传递机制阻碍了这些模型利用定向图中实体之间的隐藏互信息。此外，大多数GraIL-based模型中的外层关系提取限制模型从提取足够的用于推理的特征信息。因此，这些模型的表达能力是有限的。为了解决这些问题，我们提出了一种新的GraIL-based关联推理模型，称为MINES，通过在邻居增强子图引入消息交互机制来实现。具体来说，消息交互机制旨在捕捉未包含的隐藏互信息。它通过在 uni-directed RGCN 层之间插入一个无向/双向GCN层，将连接实体的信息交互引入双向信息交互。此外，受其他基于图的任务中增加邻居成功的经验启发，我们扩展了邻居增强子图周围的邻域，以提高基于归纳的关系推理的信息收集。对十二个基于归纳的基准数据集进行了广泛的实验，结果表明，我们的 MINES 比现有的最先进的模型表现更好，并展示了我们的消息交互机制和推理在邻居增强子图方面的有效性。
URL
https://arxiv.org/abs/2305.14074
