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.
Authors
- Piotr Formanowicz (ORCID: https://orcid.org/0000-0003-2719-3677)
- Raúl Palma
- Adam Aron Rynkiewicz (ORCID: https://orcid.org/0000-0002-0528-7544)
Institutions
- Poznan Supercomputing and Networking Center (PL)
- Poznań University of Technology (PL)
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