Entity alignment (EA) aims to identify entities across different knowledge
graphs that represent the same real-world objects. Recent embedding-based EA
methods have achieved state-of-the-art performance in EA yet faced
interpretability challenges as they purely rely on the embedding distance and
neglect the logic rules behind a pair of aligned entities. In this paper, we
propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic
rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct
Align-Subgraphs and spreads along the paths across KGs, which distinguishes it
from the embedding-based methods. Furthermore, we design an interpretable
Path-based Graph Neural Network, ASGNN, to effectively identify and integrate
the logic rules across KGs. We also introduce a node-level multi-modal
attention mechanism coupled with multi-modal enriched anchors to augment the
Align-Subgraph. Our experimental results demonstrate the superior performance
of ASGEA over the existing embedding-based methods in both EA and Multi-Modal
EA (MMEA) tasks. Our code will be available soon.

利用 Align-Subgraph 实体对齐（ASGEA）框架从 Align-Subgraphs 中挖掘逻辑规则，设计了 Path-based 图神经网络（ASGNN）来有效地在知识图谱中识别和整合逻辑规则，并通过多模式增强的锚点和多模式注意机制增强 Align-Subgraph，优于嵌入式方法在实体对齐和多模式实体对齐任务中表现出卓越性能。