Contextual Augmentation for Entity Linking using Large Language Models
Daniel Vollmers, Hamada Zahera, Diego Moussallem, Axel-Cyrille Ngonga Ngomo
Abstract
Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, which can be computationally intensive and less effective. We propose a fine-tuned model that jointly integrates entity recognition and disambiguation in a unified framework. Furthermore, our approach leverages large language models to enrich the context of entity mentions, yielding better disambiguation. We evaluated our approach on benchmark datasets and compared with several baselines. The evaluation results show that our approach achieves state-of-the-art performance on out-of-domain datasets.
BibTeX
@inproceedings{vollmers-etal-2025-contextual,
title = "Contextual Augmentation for Entity Linking using Large Language Models",
author = "Vollmers, Daniel and
Zahera, Hamada and
Moussallem, Diego and
Ngonga Ngomo, Axel-Cyrille",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.570/",
pages = "8535--8545"
}