# BELB: a Biomedical Entity Linking Benchmark

arXiv: https://arxiv.org/abs/2308.11537 · HTML: https://mlsanity.com/paper/2308.11537/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [BERN2: an advanced neural biomedical named entity recognition and normalization tool](https://mlsanity.com/paper/2201.02080/) (2022)
- [Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning](https://mlsanity.com/paper/2204.05164/) (2022)
- [Knowledge-Rich Self-Supervision for Biomedical Entity Linking](https://mlsanity.com/paper/2112.07887/) (2021)
- [Entity Linking and Discovery via Arborescence-based Supervised Clustering](https://mlsanity.com/paper/2109.01242/) (2021)
- [Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text](https://mlsanity.com/paper/2110.08228/) (2021)
- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (2021)
- [A Lightweight Neural Model for Biomedical Entity Linking](https://mlsanity.com/paper/2012.08844/) (2020)
- [tmVar 3.0: an improved variant concept recognition and normalization tool](https://mlsanity.com/paper/2204.03637/) (2022)

## Built on by (most cited first, 7 of 7)

- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation](https://mlsanity.com/paper/2401.05125/) (2024)
- [xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization](https://mlsanity.com/paper/2310.11275/) (2023)
- [Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions](https://mlsanity.com/paper/2410.07951/) (2024)
- [Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT Model on an Automatically Generated Wikipedia Corpus](https://mlsanity.com/paper/2405.11941/) (2024)
- [MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking](https://mlsanity.com/paper/2511.10887/) (2025)
- [BELXTR: Biomedical Entity Linking via Contextualized Token Retrieval](https://mlsanity.com/paper/2609.25859/) (2026)

## Abstract (arXiv)

Biomedical entity linking (BEL) is the task of grounding entity mentions to a knowledge base. It plays a vital role in information extraction pipelines for the life sciences literature. We review recent work in the field and find that, as the task is absent from existing benchmarks for biomedical text mining, different studies adopt different experimental setups making comparisons based on published numbers problematic. Furthermore, neural systems are tested primarily on instances linked to the broad coverage knowledge base UMLS, leaving their performance to more specialized ones, e.g. genes or variants, understudied. We therefore developed BELB, a Biomedical Entity Linking Benchmark, providing access in a unified format to 11 corpora linked to 7 knowledge bases and spanning six entity types: gene, disease, chemical, species, cell line and variant. BELB greatly reduces preprocessing overhead in testing BEL systems on multiple corpora offering a standardized testbed for reproducible experiments. Using BELB we perform an extensive evaluation of six rule-based entity-specific systems and three recent neural approaches leveraging pre-trained language models. Our results reveal a mixed picture showing that neural approaches fail to perform consistently across entity types, highlighting the need of further studies towards entity-agnostic models.