Knowledge graph embedding, which aims to represent entities and relations as
low dimensional vectors (or matrices, tensors, etc.), has been shown to be a
powerful technique for predicting missing links in knowledge graphs. Existing
knowledge graph embedding models mainly focus on modeling relation patterns
such as symmetry/antisymmetry, inversion, and composition. However, many
existing approaches fail to model semantic hierarchies, which are common in
real-world applications. To address this challenge, we propose a novel
knowledge graph embedding model -- namely, Hierarchy-Aware Knowledge Graph
Embedding (HAKE) -- which maps entities into the polar coordinate system. HAKE
is inspired by the fact that concentric circles in the polar coordinate system
can naturally reflect the hierarchy. Specifically, the radial coordinate aims
to model entities at different levels of the hierarchy, and entities with
smaller radii are expected to be at higher levels; the angular coordinate aims
to distinguish entities at the same level of the hierarchy, and these entities
are expected to have roughly the same radii but different angles. Experiments
demonstrate that HAKE can effectively model the semantic hierarchies in
knowledge graphs, and significantly outperforms existing state-of-the-art
methods on benchmark datasets for the link prediction task.

该研究提出了一种新的知识图谱嵌入模型 ——HAKE，该模型采用极坐标系将实体映射到不同的层次，以模拟语义层次结构，从而在链接预测任务上实现了显著的性能提升。