Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
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 fo-cused 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,which effec-tively 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.
万方数据
维普
