Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs' capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA's superior performance, highlighting LLMs' potential in facilitating EA tasks.

翻译：实体对齐（EA）对于整合多样化的知识图谱（KG）数据至关重要，在数据驱动的AI应用中扮演着关键角色。传统的EA方法主要依赖比较实体嵌入，但其有效性受到有限输入KG数据和表征学习技术能力的制约。在此背景下，我们提出了ChatEA，一个创新框架，该框架融合大型语言模型（LLMs）来改进EA。为解决输入KG数据有限的限制，ChatEA引入了一个KG-代码翻译模块，将KG结构转换为LLMs可理解的格式，从而使LLMs能够利用其广泛的背景知识来提升EA准确性。为克服对实体嵌入比较的过度依赖，ChatEA实施了一种两阶段EA策略，该策略利用LLMs在对话格式中进行多步推理的能力，从而在保持效率的同时提升准确性。我们的实验结果证实了ChatEA的卓越性能，凸显了LLMs在促进EA任务中的潜力。