Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
This addresses efficiency problems for researchers and practitioners using KGEs, though it appears incremental as it builds on existing KGE paradigms.
The paper tackles the high computational cost and environmental impact of Knowledge Graph Embedding (KGE) methods by proposing a framework that reduces training time and carbon footprint by orders of magnitude while maintaining competitive performance.
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.