To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved more advanced results via exploiting the structure information on KGs with Graph Convolutional Networks (GCN). However, we observe that entity embeddings aggregated from neighbors in different directions are just simply averaged to complete single-tasks by existing GCN based models, ignoring the specific requirements of forward and backward sub-tasks. In this paper, we propose a Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction information, the multi-head self-attention is applied to specifically combine embeddings in different directions based on various entities and sub-tasks, the geometric constraints are imposed to adjust the distribution of embeddings, and the traditional binary cross-entropy loss is modified to reflect the triple uncertainty. Moreover, the competitive experiments results on several benchmark datasets verify the effectiveness of our model.

本文提出了一种使用多头自注意力模型，对不同方向上的实体嵌入进行特定组合以完成前向和后向子任务的方向感知多任务图卷积网络模型（DsMtGCN），该模型采用了几何约束来调整嵌入分布，修改了传统的二元交叉熵损失以反映三元组的不确定性，并在几个基准数据集上进行了竞争实验来验证我们模型的有效性。

DsMtGCN：面向知识图谱完成的方向敏感多任务框架