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    <abstract>Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world objects. Traditional EA methods typically embed entity information into vector space under the guidance of seed entity pairs, and align entities by calculating and comparing the similarity between entity embeddings. With the advent of large language models (LLMs), emerging methods are increasingly integrating LLMs with traditional methods to leverage external knowledge and improve EA accuracy. However, this integration also introduces additional computational complexity and operational overhead, and still requires seed pairs that are scarce and expensive to obtain. To address these challenges, we propose EasyEA, the first end-to-end EA framework based on LLMs that requires no training. EasyEA consists of three main stages: (1) Information Summarization, (2) Embedding and Feature Fusion, and (3) Candidate Selection. By automating the EA process, EasyEA significantly reduces the reliance on seed entity pairs while demonstrating superior performance across various datasets, covering crosslingual, sparse, large-scale, and heterogeneous scenarios. Extensive experimental results show that EasyEA not only simplifies the EA process but also achieves state-of-the-art (SOTA) performance on diverse datasets, providing a promising solution for advancing EA tasks.</abstract>
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%0 Conference Proceedings
%T EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs
%A Cheng, Jingwei
%A Lu, Chenglong
%A Yang, Linyan
%A Chen, Guoqing
%A Zhang, Fu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F cheng-etal-2025-easyea
%X Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world objects. Traditional EA methods typically embed entity information into vector space under the guidance of seed entity pairs, and align entities by calculating and comparing the similarity between entity embeddings. With the advent of large language models (LLMs), emerging methods are increasingly integrating LLMs with traditional methods to leverage external knowledge and improve EA accuracy. However, this integration also introduces additional computational complexity and operational overhead, and still requires seed pairs that are scarce and expensive to obtain. To address these challenges, we propose EasyEA, the first end-to-end EA framework based on LLMs that requires no training. EasyEA consists of three main stages: (1) Information Summarization, (2) Embedding and Feature Fusion, and (3) Candidate Selection. By automating the EA process, EasyEA significantly reduces the reliance on seed entity pairs while demonstrating superior performance across various datasets, covering crosslingual, sparse, large-scale, and heterogeneous scenarios. Extensive experimental results show that EasyEA not only simplifies the EA process but also achieves state-of-the-art (SOTA) performance on diverse datasets, providing a promising solution for advancing EA tasks.
%R 10.18653/v1/2025.findings-acl.1080
%U https://aclanthology.org/2025.findings-acl.1080/
%U https://doi.org/10.18653/v1/2025.findings-acl.1080
%P 20981-20995

Markdown (Informal)

[EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs](https://aclanthology.org/2025.findings-acl.1080/) (Cheng et al., Findings 2025)

• EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (Cheng et al., Findings 2025)

ACL

• Jingwei Cheng, Chenglong Lu, Linyan Yang, Guoqing Chen, and Fu Zhang. 2025. EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs. In Findings of the Association for Computational Linguistics: ACL 2025, pages 20981–20995, Vienna, Austria. Association for Computational Linguistics.