We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and relation-focused graph structures. In particular, given the knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. In addition, 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 competitive baselines, obtaining state-of-the-art performances on seven benchmark datasets for knowledge graph completion.
翻译:我们提出了一个有效的以GNN为基础的知识图嵌入模型,名为WGE,以捕捉实体和以关系为重点的图表结构,特别是,考虑到知识图,WGE建立一个单一的、以实体为重点的、以实体为重点的无方向图表,将实体视为节点,此外,WGE还从以关系为重点的制约因素中构建了另一个单一的无方向图表,将实体和关系视为节点。WGE随后提议了一个以GNN为基础的架构,以更好地学习各实体的病媒表现和这两个单一实体和以关系为重点的图表之间的关系。WGE将学习的主体和关系表述转化为加权得分函数,以回报完成知识图的三分。实验结果表明,WGEGE在完成知识图时,超越了竞争基线,在7个基准数据集上取得了最新业绩,以完成知识图。