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.

本文提出一种基于一致性约束的知识图谱表征学习框架 ACK-MMEA，该框架通过融合一致的对齐知识来弥补因特定模式上对齐实体具有不同数量的属性而产生的上下文差距问题。我们的后续实验表明，相较于竞争方法，本文所提出的方法在两个基准数据集上取得了良好的性能。