  Knowledge Graph Embedding (KGE), which projects entities and relations into
continuous vector spaces, has garnered significant attention. Although
high-dimensional KGE methods offer better performance, they come at the expense
of significant computation and memory overheads. Decreasing embedding
dimensions significantly deteriorates model performance. While several recent
efforts utilize knowledge distillation or non-Euclidean representation learning
to augment the effectiveness of low-dimensional KGE, they either necessitate a
pre-trained high-dimensional teacher model or involve complex non-Euclidean
operations, thereby incurring considerable additional computational costs. To
address this, this work proposes Confidence-aware Self-Knowledge Distillation
(CSD) that learns from the model itself to enhance KGE in a low-dimensional
space. Specifically, CSD extracts knowledge from embeddings in previous
iterations, which would be utilized to supervise the learning of the model in
the next iterations. Moreover, a specific semantic module is developed to
filter reliable knowledge by estimating the confidence of previously learned
embeddings. This straightforward strategy bypasses the need for time-consuming
pre-training of teacher models and can be integrated into various KGE methods
to improve their performance. Our comprehensive experiments on six KGE
backbones and four datasets underscore the effectiveness of the proposed CSD.
