MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

Zhuo Chen; Jiaoyan Chen; Wen Zhang; Lingbing Guo; Yin Fang; Yufeng Huang; Yichi Zhang; Yuxia Geng; Jeff Z. Pan; Wenting Song; Huajun Chen

arXiv · 2212.14454

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

Zhuo Chen·Jiaoyan Chen·Wen Zhang·Lingbing Guo·Yin Fang·Yufeng Huang·Yichi Zhang·Yuxia Geng·Jeff Z. Pan·Wenting Song·Huajun Chen

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Abstract

Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modality fusion strategies for multi-modal entity representation, which ignores the variations of modality preferences of different entities, thus compromising robustness against noise in modalities such as blurry images and relations. This paper introduces MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for more fine-grained entity-level modality fusion and alignment. Experimental results demonstrate that our model not only achieves SOTA performance in multiple training scenarios, including supervised, unsupervised, iterative, and low-resource settings, but also has a limited number of parameters, efficient runtime, and interpretability. Our code is available at https://github.com/zjukg/MEAformer.

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BibTeX RIS

Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen. 2022-12-29. MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid. https://arxiv.org/abs/2212.14454

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