We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms strong baselines on seven benchmark datasets for knowledge graph completion.

翻译：我们提出了一种有效的基于图神经网络（GNN）的知识图谱嵌入模型，命名为WGE，以捕捉以实体和关系为中心的图结构。给定一个知识图谱，WGE构建一个以实体为节点的单一无向实体中心图。WGE还从关系中心约束中构建另一个以实体和关系为节点的单一无向图。随后，WGE提出一种基于GNN的架构，用于从这两个单一实体中心和关系中心图中更好地学习实体和关系的向量表示。WGE将学习到的实体和关系表示输入加权评分函数，以生成知识图谱补全的三元组评分。实验结果表明，WGE在七个基准数据集上的知识图谱补全任务中优于强基线方法。