  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.
