  Knowledge graph embedding (KGE) has shown great potential in automatic
knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE
models suffer from high training cost and large storage space, thus limiting
their practicality in real-world applications. To address this challenge, based
on the latest findings in the field of Contrastive Learning, we propose a novel
KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE).
Instead of the traditional Negative Sampling, we design a new loss function
based on query sampling that can balance two important training targets,
Alignment and Uniformity. Furthermore, we analyze the hardness-aware ability of
recent low-dimensional hyperbolic models and propose a lightweight
hardness-aware activation mechanism. The experimental results show that in the
limited training time, HaLE can effectively improve the performance and
training speed of KGE models on five commonly-used datasets. After training
just a few minutes, the HaLE-trained models are competitive compared to the
state-of-the-art models in both low- and high-dimensional conditions.
