  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
https://github.com/chensyCN/llm4ea_official.
