We propose a novel technique to enhance Knowledge Graph Reasoning by combining Graph Convolution Neural Network (GCN) with the Attention Mechanism. This approach utilizes the Attention Mechanism to examine the relationships between entities and their neighboring nodes, which helps to develop detailed feature vectors for each entity. The GCN uses shared parameters to effectively represent the characteristics of adjacent entities. We first learn the similarity of entities for node representation learning. By integrating the attributes of the entities and their interactions, this method generates extensive implicit feature vectors for each entity, improving performance in tasks including entity classification and link prediction, outperforming traditional neural network models. To conclude, this work provides crucial methodological support for a range of applications, such as search engines, question-answering systems, recommendation systems, and data integration tasks.

翻译：我们提出了一项新颖的技术，通过将图卷积神经网络与注意力机制相结合来增强知识图谱推理。该方法利用注意力机制考察实体与其相邻节点之间的关系，从而为每个实体构建详细的特征向量。图卷积网络通过共享参数有效表示相邻实体的特征。首先，我们学习实体的相似性以进行节点表示学习。通过整合实体的属性及其交互作用，该方法为每个实体生成了丰富的隐式特征向量，在实体分类和链接预测等任务中提升了性能，超越了传统神经网络模型。总之，本工作为搜索引擎、问答系统、推荐系统及数据集成等一系列应用提供了关键的方法论支持。