关键词: 知识图谱嵌入, 残差学习, 卷积神经网络, 链接预测

Abstract: Knowledge graph embedding projects entities and relations into a continuous low-dimensional embedding space to learn the triple features. The model based on translation cannot extract deep knowledge and has limited feature expression ability. Although the model based on neural network can extract deep knowledge, it is easy to lose shallow knowledge, and has weak feature interaction ability between entities and relations. In order to fully extract the shallow and deep features of triple in the model based on neural network, this paper introduces a knowledge graph embedding model incorporating multi-level convolutional neural networks called ConvM. ConvM model uses the recombination embedding method of cross-arrangement of head entities and relations to enhance the feature interaction between them. It also adopts the feature extraction module that combines dilated convolution with one-dimensional and three-dimensional convolution kernels to capture multiscale interaction features between entities and relations. In addition, ConvM model introduces a residual connection to improve the forgetting problem of original information. Five public datasets serve as the basis for conducting link prediction experiments. Experimental results demonstrate that ConvM model outperforms ConvE model, with MRR metric improved by 23.3%, 10.8%, and 12.2% on FB15k, FB15k-237, and Kinship datasets, respectively. These findings highlight the outstanding feature expression capability of ConvM model and its effective enhancement of link prediction performance.

Key words: knowledge graph embedding, residual learning, convolutional neural network, link prediction