Multi-scale dilated convolutional network for knowledge graph embedding

It aims to learn how to represent the low-dimensional vectors of entities and relations using observed triplets. KGE can benefit a variety of downstream tasks, such as KG completion and triplet classification. Deep models achieve state-of-the-art performance by leveraging the powerful nonlinear fitting ability of neural networks. However, most existing methods ignore multi-scale interaction features between entities and relations except InceptionE, which is hard to train because of high computation costs. In this paper, we propose a new KGE model called MDCE, that uses multi-scale dilated convolution to capture rich interaction features at different scales. Meanwhile, MDCE has lower computation costs than InceptionE. We perform extensive experiments on multiple benchmark datasets; results on the link prediction task show that the proposed model MDCE not only significantly outperforms existing state-of-the-art models but is also efficient and robust.