  Recent knowledge graph embedding (KGE) models based on hyperbolic geometry
have shown great potential in a low-dimensional embedding space. However, the
necessity of hyperbolic space in KGE is still questionable, because the
calculation based on hyperbolic geometry is much more complicated than
Euclidean operations. In this paper, based on the state-of-the-art
hyperbolic-based model RotH, we develop two lightweight Euclidean-based models,
called RotL and Rot2L. The RotL model simplifies the hyperbolic operations
while keeping the flexible normalization effect. Utilizing a novel two-layer
stacked transformation and based on RotL, the Rot2L model obtains an improved
representation capability, yet costs fewer parameters and calculations than
RotH. The experiments on link prediction show that Rot2L achieves the
state-of-the-art performance on two widely-used datasets in low-dimensional
knowledge graph embeddings. Furthermore, RotL achieves similar performance as
RotH but only requires half of the training time.
