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    <abstract>Entity Linking and Entity Disambiguation systems aim to link entity mentions to their corresponding entries, typically represented by descriptions within a predefined, static knowledge base. Current models assume that these knowledge bases are complete and up-to-date, rendering them incapable of handling entities not yet included therein. However, in an ever-evolving world, new entities emerge regularly, making these static resources insufficient for practical applications. To address this limitation, we introduce RAED, a model that retrieves external knowledge to improve factual grounding in entity descriptions. Using sources such as Wikipedia, RAED effectively disambiguates entities and bases their descriptions on factual information, reducing the dependence on parametric knowledge. Our experiments show that retrieval not only enhances overall description quality metrics, but also reduces hallucinations. Moreover, despite not relying on fixed entity inventories, RAED outperforms systems that require predefined candidate sets at inference time on Entity Disambiguation. Finally, we show that descriptions generated by RAED provide useful entity representations for downstream Entity Linking models, leading to improved performance in the extremely challenging Emerging Entity Linking task.</abstract>
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%0 Conference Proceedings
%T RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation
%A Ghonim, Karim
%A Huguet Cabot, Pere-Lluís
%A Orlando, Riccardo
%A Navigli, Roberto
%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 ghonim-etal-2025-raed
%X Entity Linking and Entity Disambiguation systems aim to link entity mentions to their corresponding entries, typically represented by descriptions within a predefined, static knowledge base. Current models assume that these knowledge bases are complete and up-to-date, rendering them incapable of handling entities not yet included therein. However, in an ever-evolving world, new entities emerge regularly, making these static resources insufficient for practical applications. To address this limitation, we introduce RAED, a model that retrieves external knowledge to improve factual grounding in entity descriptions. Using sources such as Wikipedia, RAED effectively disambiguates entities and bases their descriptions on factual information, reducing the dependence on parametric knowledge. Our experiments show that retrieval not only enhances overall description quality metrics, but also reduces hallucinations. Moreover, despite not relying on fixed entity inventories, RAED outperforms systems that require predefined candidate sets at inference time on Entity Disambiguation. Finally, we show that descriptions generated by RAED provide useful entity representations for downstream Entity Linking models, leading to improved performance in the extremely challenging Emerging Entity Linking task.
%U https://aclanthology.org/2025.emnlp-main.1746/
%P 34427-34440

Markdown (Informal)

[RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation](https://aclanthology.org/2025.emnlp-main.1746/) (Ghonim et al., EMNLP 2025)

• RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation (Ghonim et al., EMNLP 2025)

ACL

• Karim Ghonim, Pere-Lluís Huguet Cabot, Riccardo Orlando, and Roberto Navigli. 2025. RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 34427–34440, Suzhou, China. Association for Computational Linguistics.