  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.
