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Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths

Authors :
Zhu, Bolin
Liu, Xiaoze
Mao, Xin
Chen, Zhuo
Guo, Lingbing
Gui, Tao
Zhang, Qi
Publication Year :
2023

Abstract

The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG. The majority of EA methods have primarily focused on the structural modality of KGs, lacking exploration of multi-modal information. A few multi-modal EA methods have made good attempts in this field. Still, they have two shortcomings: (1) inconsistent and inefficient modality modeling that designs complex and distinct models for each modality; (2) ineffective modality fusion due to the heterogeneous nature of modalities in EA. To tackle these challenges, we propose PathFusion, consisting of two main components: (1) MSP, a unified modeling approach that simplifies the alignment process by constructing paths connecting entities and modality nodes to represent multiple modalities; (2) IRF, an iterative fusion method that effectively combines information from different modalities using the path as an information carrier. Experimental results on real-world datasets demonstrate the superiority of PathFusion over state-of-the-art methods, with 22.4%-28.9% absolute improvement on Hits@1, and 0.194-0.245 absolute improvement on MRR.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2310.05364
Document Type :
Working Paper