  The multi-modal entity alignment (MMEA) aims to find all equivalent entity
pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and
neighboring entities are valuable for the alignment task, but existing works
ignore contextual gap problems that the aligned entities have different numbers
of attributes on specific modality when learning entity representations. In
this paper, we propose a novel attribute-consistent knowledge graph
representation learning framework for MMEA (ACK-MMEA) to compensate the
contextual gaps through incorporating consistent alignment knowledge.
Attribute-consistent KGs (ACKGs) are first constructed via multi-modal
attribute uniformization with merge and generate operators so that each entity
has one and only one uniform feature in each modality. The ACKGs are then fed
into a relation-aware graph neural network with random dropouts, to obtain
aggregated relation representations and robust entity representations. In order
to evaluate the ACK-MMEA facilitated for entity alignment, we specially design
a joint alignment loss for both entity and attribute evaluation. Extensive
experiments conducted on two benchmark datasets show that our approach achieves
excellent performance compared to its competitors.
