IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment

• URL: http://arxiv.org/abs/2407.19302v1
• Date: Sat, 27 Jul 2024 17:12:37 GMT
• Title: IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
• Authors: Taoyu Su, Jiawei Sheng, Shicheng Wang, Xinghua Zhang, Hongbo Xu, Tingwen Liu,
• Abstract summary: Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs) We devise multi-modal variational encoders to generate modal-specific entity representations as probability distributions. We also propose four modal-specific information bottleneck regularizers, limiting the misleading clues in refining modal-specific entity representations.
• Score: 17.570243718626994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs), where the entities can be associated with related images. Most existing studies integrate multi-modal information heavily relying on the automatically-learned fusion module, rarely suppressing the redundant information for MMEA explicitly. To this end, we explore variational information bottleneck for multi-modal entity alignment (IBMEA), which emphasizes the alignment-relevant information and suppresses the alignment-irrelevant information in generating entity representations. Specifically, we devise multi-modal variational encoders to generate modal-specific entity representations as probability distributions. Then, we propose four modal-specific information bottleneck regularizers, limiting the misleading clues in refining modal-specific entity representations. Finally, we propose a modal-hybrid information contrastive regularizer to integrate all the refined modal-specific representations, enhancing the entity similarity between MMKGs to achieve MMEA. We conduct extensive experiments on two cross-KG and three bilingual MMEA datasets. Experimental results demonstrate that our model consistently outperforms previous state-of-the-art methods, and also shows promising and robust performance in low-resource and high-noise data scenarios.

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