  Many AI-related tasks involve the interactions of data in multiple
modalities. It has been a new trend to merge multi-modal information into
knowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG). However,
MMKGs usually suffer from low coverage and incompleteness. To mitigate this
problem, a viable approach is to integrate complementary knowledge from other
MMKGs. To this end, although existing entity alignment approaches could be
adopted, they operate in the Euclidean space, and the resulting Euclidean
entity representations can lead to large distortion of KG's hierarchical
structure. Besides, the visual information has yet not been well exploited. In
response to these issues, in this work, we propose a novel multi-modal entity
alignment approach, Hyperbolic multi-modal entity alignment(HMEA), which
extends the Euclidean representation to hyperboloid manifold. We first adopt
the Hyperbolic Graph Convolutional Networks (HGCNs) to learn structural
representations of entities. Regarding the visual information, we generate
image embeddings using the densenet model, which are also projected into the
hyperbolic space using HGCNs. Finally, we combine the structure and visual
representations in the hyperbolic space and use the aggregated embeddings to
predict potential alignment results. Extensive experiments and ablation studies
demonstrate the effectiveness of our proposed model and its components.
