Abstract：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.
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