Abstract
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.
Abstract (translated)
知识图(KG)嵌入的主要目标是学习实体和关系的低维表示，实现对缺失事实的预测。实现更好的KG嵌入的一个 significant 挑战是捕获关系模式，包括对称性、反对称性、翻转、互操作性组合、非互操作性组合、层次和多重性。本研究介绍了一种名为3H-TH(3D旋转和Translation in Hyperbolic Space)的新模型，它可以同时捕捉这些关系模式。与前几次尝试不同，之前的方法没有同时实现所有提到的属性的满意表现。实验结果表明，新模型在低维空间中的准确性、层次属性和其他关系模式方面表现更好，而在高维空间中类似地表现。
URL
https://arxiv.org/abs/2305.13015
