  Knowledge graph embedding is an important task and it will benefit lots of
downstream applications. Currently, deep neural networks based methods achieve
state-of-the-art performance. However, most of these existing methods are very
complex and need much time for training and inference. To address this issue,
we propose a simple but effective atrous convolution based knowledge graph
embedding method. Compared with existing state-of-the-art methods, our method
has following main characteristics. First, it effectively increases feature
interactions by using atrous convolutions. Second, to address the original
information forgotten issue and vanishing/exploding gradient issue, it uses the
residual learning method. Third, it has simpler structure but much higher
parameter efficiency. We evaluate our method on six benchmark datasets with
different evaluation metrics. Extensive experiments show that our model is very
effective. On these diverse datasets, it achieves better results than the
compared state-of-the-art methods on most of evaluation metrics. The source
codes of our model could be found at https://github.com/neukg/AcrE.
