Rethinking Regularization Methods for Knowledge Graph Completion
This work addresses the problem of improving KGC model performance for researchers and practitioners, though it appears incremental as it builds on existing regularization concepts.
The paper tackles the underutilization of regularization in knowledge graph completion (KGC) by showing that carefully designed regularization alleviates overfitting and enables models to exceed their original performance bounds, with a novel sparse-regularization method achieving better results than other methods in experiments.
Knowledge graph completion (KGC) has attracted considerable attention in recent years because it is critical to improving the quality of knowledge graphs. Researchers have continuously explored various models. However, most previous efforts have neglected to take advantage of regularization from a deeper perspective and therefore have not been used to their full potential. This paper rethinks the application of regularization methods in KGC. Through extensive empirical studies on various KGC models, we find that carefully designed regularization not only alleviates overfitting and reduces variance but also enables these models to break through the upper bounds of their original performance. Furthermore, we introduce a novel sparse-regularization method that embeds the concept of rank-based selective sparsity into the KGC regularizer. The core idea is to selectively penalize those components with significant features in the embedding vector, thus effectively ignoring many components that contribute little and may only represent noise. Various comparative experiments on multiple datasets and multiple models show that the SPR regularization method is better than other regularization methods and can enable the KGC model to further break through the performance margin.