Multi-modal entity alignment (MMEA) aims to identify equivalent entity pairs
across different multi-modal knowledge graphs (MMKGs). Existing approaches
focus on how to better encode and aggregate information from different
modalities. However, it is not trivial to leverage multi-modal knowledge in
entity alignment due to the modal heterogeneity. In this paper, we propose a
Multi-Grained Interaction framework for Multi-Modal Entity Alignment (MIMEA),
which effectively realizes multi-granular interaction within the same modality
or between different modalities. MIMEA is composed of four modules: i) a
Multi-modal Knowledge Embedding module, which extracts modality-specific
representations with multiple individual encoders; ii) a Probability-guided
Modal Fusion module, which employs a probability guided approach to integrate
uni-modal representations into joint-modal embeddings, while considering the
interaction between uni-modal representations; iii) an Optimal Transport Modal
Alignment module, which introduces an optimal transport mechanism to encourage
the interaction between uni-modal and joint-modal embeddings; iv) a
Modal-adaptive Contrastive Learning module, which distinguishes the embeddings
of equivalent entities from those of non-equivalent ones, for each modality.
Extensive experiments conducted on two real-world datasets demonstrate the
strong performance of MIMEA compared to the SoTA. Datasets and code have been
submitted as supplementary materials.

提出了一种用于多模态实体对齐的多粒度交互框架（MIMEA），其能够在同一模态或不同模态之间有效实现多粒度交互，并通过四个模块来完成：多模态知识嵌入模块、概率引导的模态融合模块、最优传输模态对齐模块和模态自适应对比学习模块。与其他方法相比，MIMEA 在两个真实世界数据集上进行的大量实验证明了其较强的性能。