基于图对比注意力网络的知识图谱补全

知识图谱(KG)补全旨在通过知识库中已知三元组来预测缺失的链接。由于大多数方法都是独立地处理三元组，而忽略了知识图谱所具有的异质结构和相邻节点中固有的丰富的信息，导致不能充分挖掘三元组的特征。考虑基于端到端的知识图谱补全任务，提出了一种图对比注意力网络(GCAT)，通过注意力机制同时捕获局部邻域内实体和关系的特征，并封装实体邻域上下文信息。为了有效封装三元组特征，引入一个子图级别的对比训练对象用于增强生成的实体嵌入的质量。为了验证GCAT的有效性，在链接预测任务上评估了所提方法，实验结果表明，在数据集FB15k-237中，MRR比InteractE提高0.005，比A2N模型提高0.042;在数据集WN18RR中，MRR比InteractE提高0.019，比A2N模型提高0.032。实验证明提出的GCAT模型能够有效预测知识图谱中缺失的链接。 

Knowledge graph completion based on graph contrastive attention network

Knowledge graph (KG) completion aims to predict missing links based on the known triples in a knowledge base. Since most KG completion methods dealt with triples independently without capture the heterogeneous structure of KG and the rich information that was inherent the in neighbor nodes, which resulted in incomplete mining of triple features. This study revisits the end-to-end KG completion task, and proposes a novel graph contrastive attention network (GCAT), which can capture latent representations of entities and relations simultaneously through attention mechanism, and encapsulate more neighborhood context information from the entity. Specifically, to effectively encapsulate the features of triples, a subgraph-level contrastive training object is introduced, enhancing the quality of generated entity representation. To justify the effectiveness of GCAT, the proposed model is evaluated on link prediction tasks. Experimental results show that on the dataset FB15k-237, MRR of the model is 0.005 and 0.042 higher than that of InteractE and A2N, respectively, and that on the dataset WN18RR, MRR is 0.019 and 0.032 higher than that of InteractE and A2N, respectively. Experiments prove that the proposed model can effectively predict the missing links in KGs.