HyGGE: Hyperbolic graph attention network for reasoning over knowledge graphs

Recently, hyperbolic embedding has successfully demonstrated its superiority over Euclidean analogues in representing hierarchical data. As the scale-free network that usually exhibits rich hierarchical structures, knowledge graphs naturally become a field where hyperbolic embedding shows its talents. Furthermore, hyperbolic embedding is also an expected solution to comprehensively reproduce the semantic features and underlying structures of KGs in the embedding space, which will significantly optimize the interpretability and performance of embedding models. However, most of the several existing hyperbolic studies only individually learn semantic information indicated by triples individually, making the embedding space relatively one-sided and simplified. In addition, many issues that limit reasoning performance are still ignored and unresolved in the context of hyperbolic geometry, like the response to complex relations and relation patterns. Motivated by these concerns, we propose the hyperbolic embedding model for KG reasoning, HyGGE. It is based on an innovative hyperbolic graph attention network. Furthermore, the response to complex relations, which is a well-known problem that constrains reasoning performance is also discussed in HyGGE. On the one hand, the focus on neighborhood structures and relation features makes up for the singularity that the embedding space is completely induced by triples individually, thereby optimizing the expressiveness of the embedding space. On the other hand, they cooperate with the effect of hyperbolic geometry to capture hierarchical features contained in local structures, and thus giving the hyperbolic embedding a fuller play to its advantages. Extensive experiments have validated the effectiveness and advantages of HyGGE.