In addition to Lie group manifolds, other types of manifolds or geometric structures can be explored to mitigate heterogeneity in tensor decomposition based TKGE models. One potential approach is to investigate the use of Riemannian manifolds, which can capture the intrinsic geometry of the data space more effectively. By modeling the factor tensors on Riemannian manifolds, the model can account for the curvature and non-linearity of the data distribution, leading to more accurate representations. Furthermore, exploring hyperbolic spaces or graph embeddings in hyperbolic geometry can also be beneficial, especially for capturing hierarchical structures and complex relationships in knowledge graphs.