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.

翻译：多模态实体对齐（MMEA）旨在发现多模态知识图谱（MMKGs）间所有等价实体对。丰富的属性和邻近实体对该对齐任务具有重要价值，但现有方法在实体表示学习过程中忽略了上下文差距问题——即对齐实体在特定模态上拥有不同数量的属性。本文提出一种新颖的属性一致知识图谱表示学习框架（ACK-MMEA），通过融合一致性对齐知识来补偿上下文差距。首先利用多模态属性统一化操作（包含合并与生成算子）构建属性一致知识图谱（ACKGs），确保每个实体在每个模态中具有唯一且统一的特征。随后将ACKGs输入具有随机丢弃机制的关联感知图神经网络，以获得聚合的关系表示与鲁棒的实体表示。为评估ACK-MMEA对实体对齐的促进效果，我们特别设计了同时评估实体与属性的联合对齐损失函数。在两个基准数据集上的大量实验表明，本方法相较现有方法展现出卓越性能。