MlpE: Knowledge Graph Embedding with Multilayer Perceptron Networks

Abstract

Knowledge graph embedding (KGE) is an efficient method to predict missing links in knowledge graphs. Most KGE models based on convolutional neural networks have been designed for improving the ability of capturing interaction. Although these models work well, they suffered from the limited receptive field of the convolution kernel, which lead to the lack of ability to capture long-distance interactions. In this paper, we firstly illustrate the interactions between entities and relations and discuss its effect in KGE models by experiments, and then propose MlpE, which is a fully connected network with only three layers. MlpE aims to capture long-distance interactions to improve the performance of link prediction. Extensive experimental evaluations on four typical datasets WN18RR, FB15k-237, DB100k and YAGO3-10 have shown the superority of MlpE, especially in some cases MlpE can achieve the better performance with less parameters than the state-of-the-art convolution-based KGE model.