Integrating Knowledge from Knowledge Graphs and Large Language Models for Explainable Entity Alignment

Entity alignment, a critical task in integrating knowledge from multiple knowledge graphs (KGs), aims to identify equivalent entities across different KGs. Traditional approaches predominantly rely on knowledge embedding models to generate entity representations and compute similarity scores for alignment. However, these methods often lack interpretability, rendering their predictions opaque to end users. Recently, large language models (LLMs) have demonstrated strong semantic reasoning capabilities and have been applied to various KG-related tasks, including entity alignment. Despite this progress, existing methods still suffer from three key limitations: inaccurate retrieval of candidate entities, noisy prompt construction, and weak interaction between the retrieval module and the LLM. To address these challenges, we propose EARAG (entity alignment-oriented retrieval-augmented generation), a novel framework that effectively integrates structured knowledge from KGs with the semantic reasoning power of LLMs. EARAG first employs a convolutional neural network (CNN)-based retriever that jointly models multiple similarity metrics and captures relative ranking information to retrieve high-quality candidate entities. It then constructs carefully designed prompts that guide the LLM to not only determine entity equivalence but also generate human-understandable explanations. Extensive experiments on benchmark datasets demonstrate that EARAG achieves state-of-the-art alignment accuracy while offering superior interpretability. These results highlight the potential of retrieval-augmented LLMs as transparent and effective solutions for real-world entity alignment tasks. Code and datasets are publicly available at: https://github.com/linyaoyang/EARAG.