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.

提出一种简单而有效的知识图谱嵌入框架，使用关系矩阵的全批量学习，基于正交 Procrustes 分析，非负采样训练等三项技术创新较大程度上减少训练时间和环境影响，同时能够生成具有高度可解释性并且富含语义的模型。