Universal entity linking

Entity linking is a process of connecting mentions of entities in a document to corresponding entries in a knowledge base. Traditional models for entity linking often require specific fine-tuning to work with knowledge bases other than those they were originally pretrained on, which limits their flexibility and scalability. Building on the concept of entity profile generation, we propose a novel approach that enables entity linking across various knowledge bases without the need for such fine-tuning. Our pipeline leverages a fine-tuned Large Language Model, a generic embedding model, and a vector store to achieve high precision on the TweekiGold and Reuters-128 datasets. Additionally, it demonstrates strong retrieval rates across the TweekiGold , Reuters-128 , and ISTEX-1000 Wikidata entity linking datasets. We also illustrate the applicability of our method to other knowledge bases, using the Agrovoc knowledge base as an example. This solution offers a more versatile and scalable approach to entity linking.

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Publication Details

Journal
Engineering Applications of Artificial Intelligence
Published
2025-09-10
DOI
https://doi.org/10.1016/j.engappai.2025.112185
Citations
2
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
4.96
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article

Universal entity linking

Piotr Formanowicz, Raúl Palma, Adam Aron Rynkiewicz
2 citations
Engineering Applications of Artificial Intelligence
Semantic Web and Ontologies
4.96
article

Universal entity linking

Piotr Formanowicz, Raúl Palma, Adam Aron Rynkiewicz
article en
2 citations

Abstract

Entity linking is a process of connecting mentions of entities in a document to corresponding entries in a knowledge base. Traditional models for entity linking often require specific fine-tuning to work with knowledge bases other than those they were originally pretrained on, which limits their flexibility and scalability. Building on the concept of entity profile generation, we propose a novel approach that enables entity linking across various knowledge bases without the need for such fine-tuning. Our pipeline leverages a fine-tuned Large Language Model, a generic embedding model, and a vector store to achieve high precision on the TweekiGold and Reuters-128 datasets. Additionally, it demonstrates strong retrieval rates across the TweekiGold , Reuters-128 , and ISTEX-1000 Wikidata entity linking datasets. We also illustrate the applicability of our method to other knowledge bases, using the Agrovoc knowledge base as an example. This solution offers a more versatile and scalable approach to entity linking.

Engineering Applications of Artificial IntelligenceVol. 161
Poznan Supercomputing and Networking Center (PL), Poznań University of Technology (PL)
Openalex Percentile: Top 5%
Semantic Web and Ontologies
4.96
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Universal entity linking — Piotr Formanowicz, Raúl Palma, et al. · Engineering Applications of Artificial Intelligence (2025) | TGRS Research Map | TGRS