Knowledge Graph Embedding with Atrous Convolution and Residual Learning

Feiliang Ren, Juchen Li, Huihui Zhang, Shilei Liu, Bochao Li, Ruicheng Ming, Yujia Bai


Abstract
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.
Anthology ID:
2020.coling-main.134
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1532–1543
Language:
URL:
https://aclanthology.org/2020.coling-main.134
DOI:
10.18653/v1/2020.coling-main.134
Bibkey:
Cite (ACL):
Feiliang Ren, Juchen Li, Huihui Zhang, Shilei Liu, Bochao Li, Ruicheng Ming, and Yujia Bai. 2020. Knowledge Graph Embedding with Atrous Convolution and Residual Learning. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1532–1543, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Knowledge Graph Embedding with Atrous Convolution and Residual Learning (Ren et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.134.pdf
Data
FB15kWN18WN18RR