Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying
equivalent entity pairs. While existing methods heavily rely on human-generated
labels, it is prohibitively expensive to incorporate cross-domain experts for
annotation in real-world scenarios. The advent of Large Language Models (LLMs)
presents new avenues for automating EA with annotations, inspired by their
comprehensive capability to process semantic information. However, it is
nontrivial to directly apply LLMs for EA since the annotation space in
real-world KGs is large. LLMs could also generate noisy labels that may mislead
the alignment. To this end, we propose a unified framework, LLM4EA, to
effectively leverage LLMs for EA. Specifically, we design a novel active
learning policy to significantly reduce the annotation space by prioritizing
the most valuable entities based on the entire inter-KG and intra-KG structure.
Moreover, we introduce an unsupervised label refiner to continuously enhance
label accuracy through in-depth probabilistic reasoning. We iteratively
optimize the policy based on the feedback from a base EA model. Extensive
experiments demonstrate the advantages of LLM4EA on four benchmark datasets in
terms of effectiveness, robustness, and efficiency. Codes are available via
this https URL

通过设计一种新颖的主动学习策略以及引入无监督标签优化方法，我们提出了一个统一的框架 LLM4EA，以有效地利用大型语言模型自动化实体对齐，并在四个基准数据集上进行了广泛实验，证明了其在效果、鲁棒性和效率方面的优势。