Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
This work improves multi-modal entity alignment for knowledge graph integration, offering a more efficient and effective solution compared to existing methods.
The paper tackled the problem of entity alignment across knowledge graphs by addressing inconsistent modality modeling and ineffective fusion, achieving a 22.4%-28.9% absolute improvement in Hits@1 and 0.194-0.245 in MRR over state-of-the-art methods.
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.