Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph Embeddings

Lower-dimensional temporal knowledge graph embedding (TKGE) models are crucial for practical applications and resource-limited scenarios, although existing models employ higher-dimensional embeddings in training. In this paper, we propose a new framework for distilling TKGE models via an easy to hard pedagogical principle. The framework utilizes a learnable curriculum temperature (CT) module to optimize and guide the knowledge distillation process dynamically, ensuring that the entire procedure adheres to the principle. It also employs a self-adaptive attention mechanism to endeavor to achieve efficient transfer of knowledge from higher-dimensional models to lower-dimensional ones. Evaluation on various TKGE models and datasets demonstrates the proposed approach significantly reduces the model's parameters without noticeably affecting its performance.