Although the Knowledge Graph (KG) has been successfully applied to various applications, there is still a large amount of incomplete knowledge in the KG. This study proposes a Knowledge Graph Completion (KGC) method based on the Graph Attention Faded Mechanism (GAFM) to solve the problem of incomplete knowledge in KG. GAFM introduces a graph attention network that incorporates the information in multi-hop neighborhood nodes to embed the target entities into low dimensional space. To generate a more expressive entity representation, GAFM gives different weights to the neighborhood nodes of the target entity by adjusting the attention value of neighborhood nodes according to the variation of the path length. The attention value is adjusted by the attention faded coefficient, which decreases with the increase of the distance between the neighborhood node and the target entity. Then, considering that the capsule network has the ability to fit features, GAFM introduces the capsule network as the decoder to extract feature information from triple representations. To verify the effectiveness of the proposed method, we conduct a series of comparative experiments on public datasets (WN18RR and FB15k-237). Experimental results show that the proposed method outperforms baseline methods. The Hits@10 metric is improved by 8% compared with the second-place KBGAT method.

虽然知识图谱（KG）已经成功地应用于各种应用，但KG中仍然存在大量不完整的知识。本研究提出了一种基于图注意力衰减机制（GAFM）的知识图补全（KGC）方法来解决KG中知识不完整的问题。GAFM 引入了一个图注意力网络，它结合了多跳邻域节点中的信息，将目标实体嵌入到低维空间中。为了生成更具表现力的实体表示，GAFM 通过根据路径长度的变化调整邻域节点的注意力值，对目标实体的邻域节点赋予不同的权重。注意力值由注意力衰减系数调整，随着邻域节点与目标实体之间距离的增加而减小。然后，考虑到胶囊网络具有拟合特征的能力，GAFM 引入胶囊网络作为解码器，从三重表示中提取特征信息。为了验证所提出方法的有效性，我们对公共数据集（WN18RR 和 FB15k-237）进行了一系列对比实验。实验结果表明，所提出的方法优于基线方法。Hits@10 指标与第二名的 KBGAT 方法相比提高了 8%。我们对公共数据集（WN18RR 和 FB15k-237）进行了一系列对比实验。实验结果表明，所提出的方法优于基线方法。Hits@10 指标与第二名的 KBGAT 方法相比提高了 8%。我们对公共数据集（WN18RR 和 FB15k-237）进行了一系列对比实验。实验结果表明，所提出的方法优于基线方法。Hits@10 指标与第二名的 KBGAT 方法相比提高了 8%。