A knowledge graph is a collection of triples, often represented in the form of “subject,” “relation,” “object.” The task of knowledge graph completion (KGC) is to automatically predict missing links by reasoning over the information already present in the knowledge graph. Recent popularization of graph neural networks has also been spread to KGC. Typical techniques like SACN achieve dramatic achievements and beat previous state-of-the-art. However, those models still lack the ability to distinguish different local structures within a graph, which leads to the over smoothing problem. In this work, we propose SD-GAT, a graph attention network with a structure-distinguishable neighborhood aggregation scheme, which models the injective function to aggregate information from the neighborhood. The model is constituted of two modules. The encoder is a graph attention network that improved with our neighborhood aggregation scheme, which could be applied for a more distinct representation of entities and relations. The decoder is a convolutional neural network using \(3\times 3\) convolution filters. Our empirical research provides an effective solution to increase the discriminative power of graph attention networks, and we show significant improvement of the proposed SD-GAT compared to the state-of-the-art methods on standard FB15K-237 and WN18RR datasets.

知识图是三元组的集合，通常以“主题”、“关系”、“对象”的形式表示。知识图谱补全 (KGC) 的任务是通过推理知识图中已经存在的信息来自动预测缺失的链接。最近图神经网络的普及也蔓延到了 KGC。像 SACN 这样的典型技术取得了巨大的成就并击败了以前的最先进技术。然而，这些模型仍然缺乏区分图中不同局部结构的能力，这导致了过度平滑问题。在这项工作中，我们提出了 SD-GAT，一种具有结构可区分邻域聚合方案的图注意力网络，它对内射函数进行建模以聚合来自邻域的信息。该模型由两个模块组成。编码器是一个图注意力网络，通过我们的邻域聚合方案进行了改进，可以应用于实体和关系的更清晰的表示。解码器是一个卷积神经网络，使用\(3\times 3\) 个卷积滤波器。我们的实证研究提供了一种有效的解决方案来提高图注意力网络的判别能力，并且与标准 FB15K-237 和 WN18RR 数据集上的最新方法相比，我们展示了所提出的 SD-GAT 的显着改进。