Knowledge graph embeddings (KGE) have been validated as powerful methods for
inferring missing links in knowledge graphs (KGs) since they map entities into
Euclidean space and treat relations as transformations of entities. Currently,
some Euclidean KGE methods model semantic hierarchies prevalent in KGs and
promote the performance of link prediction. For hierarchical data, instead of
traditional Euclidean space, hyperbolic space as an embedding space has shown
the promise of high fidelity and low memory consumption; however, existing
hyperbolic KGE methods neglect to model them. To address this issue, we propose
a novel KGE model -- hyperbolic hierarchical KGE (HypHKGE). To be specific, we
first design the attention-based learnable curvatures for hyperbolic space to
preserve rich semantic hierarchies. Moreover, we define the hyperbolic
hierarchical transformations based on the theory of hyperbolic geometry, which
utilize hierarchies that we preserved to infer the links. Experiments show that
HypHKGE can effectively model semantic hierarchies in hyperbolic space and
outperforms the state-of-the-art hyperbolic methods, especially in low
dimensions.

本文提出了一种新颖的基于注意力的可学习曲率超几何分层知识图嵌入模型（HypHKGE），以在超几何空间中有效地建模知识图中的语义层次结构，并在低维度下表现出色，特别是在链接预测方面。