  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.
