RRDGNN: Relational reflective disentangled graph neural network for entity alignment

The objective of entity alignment (EA) is to automatically discover entities across different knowledge graphs (KGs) that refer to the same real-world object, which is a crucial step for knowledge fusion. Graph neural network based methods currently dominate the EA landscape. However, the entity neighborhood often contains information about various aspects. Most of these methods treat the neighborhood of an entity as a whole, aggregating all its neighbors into a single, static representation. This can introduce noise and lead to incorrect alignment results due to the heterogeneous neighborhood structures of aligned entities. In this paper, we propose a novel relational reflective disentangled graph neural network (RRDGNN) for EA, which learns disentangled representations for different aspects of entities, thereby alleviating the prevalent issue of neighborhood heterogeneity in EA. Specifically, RRDGNN designs a relational reflective neighborhood routing mechanism for learning disentangled, dynamic entity representations at both micro and macro levels. An alignment-aware self-attention strategy is then employed to assign adaptive weights to different entity aspects. Finally, multi-aspect seed generators are co-trained in a semi-supervised learning framework. They favor entities with specific sparsity, which helps to refine the seed selection process and generate high-quality alignment seeds. Extensive experiments on five real-world datasets, along with detailed ablation studies and analyses, demonstrate that RRDGNN consistently outperforms existing EA methods.