 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 .
<meaning-changed>  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 .
 Then , we propose a novel knowledge distillation framework for knowledge graph embedding, utilizing multiple low-dimensional KGE models as teachers and two student components, namely Junior and Senior . Under a novel iterative distillation strategy, the MulDE modelproduces soft labels according to training epochs and student performance adaptively .