Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Xutan Peng, Guanyi Chen, Chenghua Lin, Mark Stevenson
Abstract
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.
BibTeX
@inproceedings{peng-etal-2021-highly,
title = "Highly Efficient Knowledge Graph Embedding Learning with {O}rthogonal {P}rocrustes {A}nalysis",
author = "Peng, Xutan and
Chen, Guanyi and
Lin, Chenghua and
Stevenson, Mark",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.187/",
doi = "10.18653/v1/2021.naacl-main.187",
pages = "2364--2375"
}