@inproceedings{chen-etal-2025-language,
title = "How do Language Models Reshape Entity Alignment? A Survey of {LM}-Driven {EA} Methods: Advances, Benchmarks, and Future",
author = "Chen, Zerui and
Fan, Huiming and
Wang, Qianyu and
He, Tao and
Liu, Ming and
Chang, Heng and
Yu, Weijiang and
Li, Ze and
Qin, Bing",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1184/",
doi = "10.18653/v1/2025.emnlp-main.1184",
pages = "23230--23245",
ISBN = "979-8-89176-332-6",
abstract = "Entity alignment (EA), critical for knowledge graph (KG) integration, identifies equivalent entities across different KGs. Traditional methods often face challenges in semantic understanding and scalability. The rise of language models (LMs), particularly large language models (LLMs), has provided powerful new strategies. This paper systematically reviews LM-driven EA methods, proposing a novel taxonomy that categorizes methods in three key stages: data preparation, feature embedding, and alignment. We further summarize key benchmarks, evaluation metrics, and discuss future directions. This paper aims to provide researchers and practitioners with a clear and comprehensive understanding of how language models reshape the field of entity alignment."
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<abstract>Entity alignment (EA), critical for knowledge graph (KG) integration, identifies equivalent entities across different KGs. Traditional methods often face challenges in semantic understanding and scalability. The rise of language models (LMs), particularly large language models (LLMs), has provided powerful new strategies. This paper systematically reviews LM-driven EA methods, proposing a novel taxonomy that categorizes methods in three key stages: data preparation, feature embedding, and alignment. We further summarize key benchmarks, evaluation metrics, and discuss future directions. This paper aims to provide researchers and practitioners with a clear and comprehensive understanding of how language models reshape the field of entity alignment.</abstract>
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%0 Conference Proceedings
%T How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future
%A Chen, Zerui
%A Fan, Huiming
%A Wang, Qianyu
%A He, Tao
%A Liu, Ming
%A Chang, Heng
%A Yu, Weijiang
%A Li, Ze
%A Qin, Bing
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chen-etal-2025-language
%X Entity alignment (EA), critical for knowledge graph (KG) integration, identifies equivalent entities across different KGs. Traditional methods often face challenges in semantic understanding and scalability. The rise of language models (LMs), particularly large language models (LLMs), has provided powerful new strategies. This paper systematically reviews LM-driven EA methods, proposing a novel taxonomy that categorizes methods in three key stages: data preparation, feature embedding, and alignment. We further summarize key benchmarks, evaluation metrics, and discuss future directions. This paper aims to provide researchers and practitioners with a clear and comprehensive understanding of how language models reshape the field of entity alignment.
%R 10.18653/v1/2025.emnlp-main.1184
%U https://aclanthology.org/2025.emnlp-main.1184/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1184
%P 23230-23245
Markdown (Informal)
[How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future](https://aclanthology.org/2025.emnlp-main.1184/) (Chen et al., EMNLP 2025)
ACL
- Zerui Chen, Huiming Fan, Qianyu Wang, Tao He, Ming Liu, Heng Chang, Weijiang Yu, Ze Li, and Bing Qin. 2025. How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 23230–23245, Suzhou, China. Association for Computational Linguistics.