Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify
equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However,
this task faces challenges due to the presence of different types of
information, including neighboring entities, multi-modal attributes, and entity
types. Directly incorporating the above information (e.g., concatenation or
attention) can lead to an unaligned information space. To address these
challenges, we propose a novel MMEA transformer, called MoAlign, that
hierarchically introduces neighbor features, multi-modal attributes, and entity
types to enhance the alignment task. Taking advantage of the transformer's
ability to better integrate multiple information, we design a hierarchical
modifiable self-attention block in a transformer encoder to preserve the unique
semantics of different information. Furthermore, we design two entity-type
prefix injection methods to integrate entity-type information using type
prefixes, which help to restrict the global information of entities not present
in the MMKGs. Our extensive experiments on benchmark datasets demonstrate that
our approach outperforms strong competitors and achieves excellent entity
alignment performance.

多模态实体对齐 (MMEA) 是一个关键任务，旨在识别多模态知识图谱 (MMKGs) 中的等效实体对。我们提出了一种名为 MoAlign 的新颖 MMEA transformer，通过引入邻居特征、多模态属性和实体类型来增强对齐任务。利用 transformer 的能力更好地集成多重信息，我们设计了一个层次可修改的自注意力块在 transformer 编码器中，以保留不同信息的独特语义。此外，我们设计了两种实体类型前缀注入方法，使用类型前缀来整合实体类型信息，有助于限制 MMKGs 中不存在的实体的全局信息。我们在基准数据集上进行了广泛实验，证明我们的方法优于强竞争对手，且实现了出色的实体对齐性能。