  Multi-modal entity alignment (MMEA) aims to identify equivalent entities
between two multi-modal knowledge graphs for integration. Unfortunately, prior
arts have attempted to improve the interaction and fusion of multi-modal
information, which have overlooked the influence of modal-specific noise and
the usage of labeled and unlabeled data in semi-supervised settings. In this
work, we introduce a Pseudo-label Calibration Multi-modal Entity Alignment
(PCMEA) in a semi-supervised way. Specifically, in order to generate holistic
entity representations, we first devise various embedding modules and attention
mechanisms to extract visual, structural, relational, and attribute features.
Different from the prior direct fusion methods, we next propose to exploit
mutual information maximization to filter the modal-specific noise and to
augment modal-invariant commonality. Then, we combine pseudo-label calibration
with momentum-based contrastive learning to make full use of the labeled and
unlabeled data, which improves the quality of pseudo-label and pulls aligned
entities closer. Finally, extensive experiments on two MMEA datasets
demonstrate the effectiveness of our PCMEA, which yields state-of-the-art
performance.
