Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary methods for entity alignment have predominantly utilized knowledge embedding models to procure entity embeddings that encapsulate various similarities-structural, relational, and attributive. These embeddings are then integrated through attention-based information fusion mechanisms. Despite this progress, effectively harnessing multifaceted information remains challenging due to inherent heterogeneity. Moreover, while Large Language Models (LLMs) have exhibited exceptional performance across diverse downstream tasks by implicitly capturing entity semantics, this implicit knowledge has yet to be exploited for entity alignment. In this study, we propose a Large Language Model-enhanced Entity Alignment framework (LLMEA), integrating structural knowledge from KGs with semantic knowledge from LLMs to enhance entity alignment. Specifically, LLMEA identifies candidate alignments for a given entity by considering both embedding similarities between entities across KGs and edit distances to a virtual equivalent entity. It then engages an LLM iteratively, posing multiple multi-choice questions to draw upon the LLM's inference capability. The final prediction of the equivalent entity is derived from the LLM's output. Experiments conducted on three public datasets reveal that LLMEA surpasses leading baseline models. Additional ablation studies underscore the efficacy of our proposed framework.

翻译：实体对齐是构建更全面知识图谱（KG）的前提，旨在识别不同知识图谱间的等价实体。当前主流方法主要利用知识嵌入模型获取实体嵌入，以捕获结构相似性、关系相似性和属性相似性，再通过基于注意力的信息融合机制整合这些嵌入。尽管取得进展，但由于固有的异质性，有效利用多维度信息仍具挑战性。此外，虽然大语言模型（LLMs）通过隐式捕获实体语义在下游任务中展现出卓越性能，但这种隐式知识尚未被用于实体对齐。本研究提出一种大语言模型增强的实体对齐框架（LLMEA），融合知识图谱的结构知识与大语言模型的语义知识以提升对齐效果。具体而言，LLMEA通过跨知识图谱实体嵌入相似度与虚拟等价实体的编辑距离，为给定实体候选对齐实例；随后迭代调用大语言模型，通过多道选择题激发其推理能力，最终根据大语言模型输出确定等价实体。在三个公开数据集上的实验表明，LLMEA优于主流基线模型，消融实验进一步验证了所提框架的有效性。