The task of link prediction aims to solve the problem of incomplete knowledge
caused by the difficulty of collecting facts from the real world. GCNs-based
models are widely applied to solve link prediction problems due to their
sophistication, but GCNs-based models are suffering from two problems in the
structure and training process. 1) The transformation methods of GCN layers
become increasingly complex in GCN-based knowledge representation models; 2)
Due to the incompleteness of the knowledge graph collection process, there are
many uncollected true facts in the labeled negative samples. Therefore, this
paper investigates the characteristic of the information aggregation
coefficient (self-attention) of adjacent nodes and redesigns the self-attention
mechanism of the GAT structure. Meanwhile, inspired by human thinking habits,
we designed a semi-supervised self-training method over pre-trained models.
Experimental results on the benchmark datasets FB15k-237 and WN18RR show that
our proposed self-attention mechanism and semi-supervised self-training method
can effectively improve the performance of the link prediction task. If you
look at FB15k-237, for example, the proposed method improves Hits@1 by about
30%.

本文提出了一种基于自我注意机制的改进图神经网络（GNN）方法以及一个基于预训练模型的半监督自我训练方法。实验结果表明这些方法都可以有效地提高链接预测任务的性能。