| Title: |
IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment |
| Authors: |
Su, Taoyu, Sheng, Jiawei, Wang, Shicheng, Zhang, Xinghua, Xu, Hongbo, Liu, Tingwen |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language, Computer Science - Multimedia |
| More Details: |
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.
Comment: Accepted by ACM MM 2024 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2407.19302 |
| Accession Number: |
edsarx.2407.19302 |
| Database: |
arXiv |
