  Multi-modal entity alignment (MMEA) aims to identify equivalent entities
between two multi-modal knowledge graphs (MMKGs), whose entities can be
associated with relational triples and related images. Most previous studies
treat the graph structure as a special modality, and fuse different modality
information with separate uni-modal encoders, neglecting valuable relational
associations in modalities. Other studies refine each uni-modal information
with graph structures, but may introduce unnecessary relations in specific
modalities. To this end, we propose a novel local-to-global interaction network
for MMEA, termed as LoginMEA. Particularly, we first fuse local multi-modal
interactions to generate holistic entity semantics and then refine them with
global relational interactions of entity neighbors. In this design, the
uni-modal information is fused adaptively, and can be refined with relations
accordingly. To enrich local interactions of multi-modal entity information, we
device modality weights and low-rank interactive fusion, allowing diverse
impacts and element-level interactions among modalities. To capture global
interactions of graph structures, we adopt relation reflection graph attention
networks, which fully capture relational associations between entities.
Extensive experiments demonstrate superior results of our method over 5
cross-KG or bilingual benchmark datasets, indicating the effectiveness of
capturing local and global interactions.
