基于Triplet注意力的循环卷积神经网络模型

Circular Convolutional Neural Networks Based on Triplet Attention

现有的融合文本或邻居信息的知识补全模型忽略文本和邻居之间的相互作用,难以捕获与实体具有较强语义相关性的信息,加上基于卷积神经网络的模型未考虑实体中的关系相关信息,导致预测性能不佳.因此,文中结合文本信息和拓扑邻居信息,提出基于Triplet注意力的循环卷积神经网络模型.首先,通过语义匹配的方式,选取文本描述中与实体具有较强语义相关性的单词.再与拓扑邻居复合作为实体邻居,增强实体表示.然后,重塑实体的融合表示和关系表示.最后,利用Triplet注意力优化卷积输入,使卷积操作能提取实体中与关系相关的特征,提升模型性能.在多个公开数据集上的链路预测实验表明,文中模型性能较优.

In the existing knowledge completion models with textual or neighbor information,the interaction between texts and neighbors is ignored.Therefore,it is difficult to capture the information with strong semantic relevance to entities.In addition,the relationship-specific information in the entities is not taken into account in the models based on convolutional neural networks,which results in poor prediction performance.In this paper,a circular convolutional neural network model based on triplet attention is proposed combining textual and neighbor information.Firstly,the words with strong semantic relevance to entities in textual descriptions are selected by semantic matching,and then they are combined with topological neighbors as entity neighbors to enhance entity representations.Next,the fusion representations of the entity and the relation representations are reshaped.Finally,the triplet attention is utilized to optimize the input of the convolution and the convolution operation can extract the features related to the relations in the entities,which improves the model performance.Experiments on several public datasets show that the performance of the proposed model is superior.