 However, recent KGE models tend to improve performance by excessively increasing vector dimensions, which would cause enormous training costs and save storage in practical applications. To address this problem, we first theoretically analyze the capacity of low-dimensional space for KG embeddings based on the principle of minimum entropy. Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
<coherence>  However, recent KGE models tend to improve performance by excessively increasing vector dimensions, which would cause enormous training costs and save storage in practical applications. To address this problem, we first theoretically analyze the capacity of low-dimensional space for KG embeddings based on the principle of minimum entropy. Then , we propose  a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional  KGE models as teachers  . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .
 However, recent KGE models tend to improve performance by excessively increasing vector dimensions, which would cause enormous training costs and require more storage space. In this paper, instead of training high-dimensional models , we propose a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional KGE models as teachers . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .