Knowledge graph completion (KGC) aims to discover missing relations of query entities. Current text-based models utilize the entity name and description to infer the tail entity given the head entity and a certain relation. Existing approaches also consider the neighborhood of the head entity. However, these methods tend to model the neighborhood using a flat structure and are only restricted to 1-hop neighbors. In this work, we propose a node neighborhood-enhanced framework for knowledge graph completion. It models the head entity neighborhood from multiple hops using graph neural networks to enrich the head node information. Moreover, we introduce an additional edge link prediction task to improve KGC. Evaluation on two public datasets shows that this framework is simple yet effective. The case study also shows that the model is able to predict explainable predictions.

翻译：知识图谱补全（KGC）旨在发现查询实体缺失的关系。现有基于文本的模型利用实体名称和描述，在给定头实体和特定关系的情况下推断尾实体。已有方法还考虑了头实体的邻域。然而，这些方法倾向于使用扁平化结构建模邻域，且仅局限于1跳邻居。本文提出一种面向知识图谱补全的节点邻域增强框架。该框架利用图神经网络建模多跳头实体邻域，以丰富头节点信息。此外，我们引入额外的边链接预测任务来改进KGC。在两个公开数据集上的评估表明，该框架简单有效。案例研究也显示该模型能生成可解释的预测结果。