SSNF: Optimizing Entity Alignment with a Novel Structural and Semantic Neighbor Filtering

In the domain of Knowledge Graphs (KGs), the alignment of entities is pivotal, aiming to identify and match equivalent entities across distinct KGs. Existing methodologies primarily aggregate information from direct neighbors via graph neural networks, a process which can inadvertently introduce noise. To address this challenge, we introduce SSNF, an innovative neighbor filtering mechanism that optimally balances structural and semantic information, crucial for accurate entity alignment. By employing motifs for structural assessment and leveraging Large Language Models (LLMs) for semantic analysis with ’Reasoning-Challenging (Re-Cha)’ strategy to query LLMs to determine important neighbors. This dual-focus strategy mitigates the inclusion of less informative neighbors. When integrated with existing Entity Alignment (EA) frameworks, our approach demonstrates superior efficacy, significantly outperforming conventional methods through meticulous neighbor selection. The extensive experiments, conducted on the most widely used benchmark datasets (i.e., DBP15K), exhibit a significant improvement in EA performance, demonstrating its potential to advance the field of KG entity alignment by synergizing structural insights and semantic precision.