The main objective of Knowledge Graph (KG) embeddings is to learn
low-dimensional representations of entities and relations, enabling the
prediction of missing facts. A significant challenge in achieving better KG
embeddings lies in capturing relation patterns, including symmetry,
antisymmetry, inversion, commutative composition, non-commutative composition,
hierarchy, and multiplicity. This study introduces a novel model called 3H-TH
(3D Rotation and Translation in Hyperbolic space) that captures these relation
patterns simultaneously. In contrast, previous attempts have not achieved
satisfactory performance across all the mentioned properties at the same time.
The experimental results demonstrate that the new model outperforms existing
state-of-the-art models in terms of accuracy, hierarchy property, and other
relation patterns in low-dimensional space, meanwhile performing similarly in
high-dimensional space.

该研究介绍了一种叫做 3H-TH 的新型模型，可以同时捕捉对称、反对称、倒置、交换组合、非交换组合、层级和多重性等许多关系模式，实验结果表明，在低维空间中，该模型在精度、层级属性和其他关系模式方面优于现有的最先进模型，但在高维空间中表现相似。