A practical and nuanced framework for entity linking evaluation

Entity linking maps textual mentions with entities in vocabularies. While accuracy is the primary metric for entity linking evaluation, it fails to capture the complexity of model behaviour. We propose an entity linking evaluation framework that clarifies the target objects of metrics calculation, incorporates term hierarchy, generates performance profiles, and summarises them as model characteristics. The framework emphasises hierarchical vocabulary structures, shifting the focus from text-level label matching to semantic comparison between concepts. We illustrate the competence and utility of this framework through a case study on disease entity linking. Our results highlight the importance of aligning evaluation metrics with application-specific requirements, and provide structured hierarchical error analysis for entity linking, paving the way for more nuanced and practical assessments of entity linking systems.

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Publication Details

Journal
Journal of Biomedical Semantics
Published
2026-02-12
DOI
https://doi.org/10.1186/s13326-025-00339-0
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
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article

A practical and nuanced framework for entity linking evaluation

Goran Nenadić, Fuqi Xu, Robert Stevens
Journal of Biomedical Semantics
Biomedical Text Mining and Ontologies
article

A practical and nuanced framework for entity linking evaluation

Goran Nenadić, Fuqi Xu, Robert Stevens
article en

Abstract

Entity linking maps textual mentions with entities in vocabularies. While accuracy is the primary metric for entity linking evaluation, it fails to capture the complexity of model behaviour. We propose an entity linking evaluation framework that clarifies the target objects of metrics calculation, incorporates term hierarchy, generates performance profiles, and summarises them as model characteristics. The framework emphasises hierarchical vocabulary structures, shifting the focus from text-level label matching to semantic comparison between concepts. We illustrate the competence and utility of this framework through a case study on disease entity linking. Our results highlight the importance of aligning evaluation metrics with application-specific requirements, and provide structured hierarchical error analysis for entity linking, paving the way for more nuanced and practical assessments of entity linking systems.

Journal of Biomedical SemanticsVol. 17(1)
University of Manchester (GB)
Quality Education
Openalex Percentile: Top 58%
Biomedical Text Mining and Ontologies
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