Abstract
Due to the general incompleteness of knowledge graphs, knowledge graph link prediction is a hot research topic for knowledge graph completion. The low-dimensional embedding of entities and relations can be realized through link prediction methods, and then inference can be made. The previous link prediction methods mainly used shallow and fast models of the knowledge graph, such as TransE, TransH, TransA and other models. The feature extraction capabilities of these models are insufficient, which affects the performance of prediction. The recently proposed method ConvE uses embedded two-dimensional convolution and multi-layer nonlinear features to model the knowledge graph, which increases the interaction between entities and relations to a certain extent. In this paper, we propose a Feature Interaction Convolutional Network (FICN) for knowledge graph embedding, which uses three methods: Random Permutation, Chequer Reshaping and Circular Convolution to increase the feature interaction capability of the model, thereby effectively improving the link prediction performance. We verified the feasibility and effectiveness of FICN on the FB15K-237, WN18RR and YAGO3-10 data sets. Through experiments, we found that on these three data sets, FICN has a certain degree of improvement in MRR score compared to ConvE, and it is also stronger than ConvE in MR, HIST@10 and HIST@1 indicators. In addition, our model increases the training speed by adding the Batch Normalization preprocessing method during convolution training. Compared with ConvE model, our model has about a 50% reduction in training time.
This work was supported by National Key R&D Program of China (No.2019YFB1404700).
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Li, J., Li, A., Liu, T. (2021). Feature Interaction Convolutional Network for Knowledge Graph Embedding. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_30
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