# Knowledge-Rich Self-Supervision for Biomedical Entity Linking

arXiv: https://arxiv.org/abs/2112.07887 · HTML: https://mlsanity.com/paper/2112.07887/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [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)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [Improving Entity Linking by Modeling Latent Relations between Mentions](https://mlsanity.com/paper/1804.10637/) (2018)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking](https://mlsanity.com/paper/1807.05127/) (2018)
- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (2021)

## Built on by (most cited first, 30 of 31)

- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)](https://mlsanity.com/paper/2210.11892/) (2022)
- [RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records](https://mlsanity.com/paper/2403.00815/) (2024)
- [Chain-of-Skills: A Configurable Model for Open-domain Question Answering](https://mlsanity.com/paper/2305.03130/) (2023)
- [PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking](https://mlsanity.com/paper/2405.07500/) (2024)
- [Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking](https://mlsanity.com/paper/2307.01137/) (2023)
- [AutoPCR: Automated Phenotype Concept Recognition by Prompting](https://mlsanity.com/paper/2507.19315/) (2025)
- [HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare](https://mlsanity.com/paper/2507.19726/) (2025)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records](https://mlsanity.com/paper/2403.09226/) (2024)
- [BELB: a Biomedical Entity Linking Benchmark](https://mlsanity.com/paper/2308.11537/) (2023)
- [Learning from Negative Samples in Biomedical Generative Entity Linking](https://mlsanity.com/paper/2408.16493/) (2024)
- [BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation](https://mlsanity.com/paper/2401.05125/) (2024)
- [Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation](https://mlsanity.com/paper/2502.21107/) (2025)
- [A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2306.10042/) (2023)
- [Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions](https://mlsanity.com/paper/2410.07951/) (2024)
- [Exploring Partial Knowledge Base Inference in Biomedical Entity Linking](https://mlsanity.com/paper/2303.10330/) (2023)
- [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)
- [CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies](https://mlsanity.com/paper/2505.04365/) (2025)
- [MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition](https://mlsanity.com/paper/2505.12964/) (2025)
- [SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking](https://mlsanity.com/paper/2601.19667/) (2026)
- [DeepProphet2 -- A Deep Learning Gene Recommendation Engine](https://mlsanity.com/paper/2208.01918/) (2022)
- [Solving the Right Problem is Key for Translational NLP: A Case Study in UMLS Vocabulary Insertion](https://mlsanity.com/paper/2311.15106/) (2023)
- [Towards Knowledge-Infused Automated Disease Diagnosis Assistant](https://mlsanity.com/paper/2405.11181/) (2024)
- [Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques](https://mlsanity.com/paper/2405.15134/) (2024)
- [Hybrid X-Linker: Automated Data Generation and Extreme Multi-label Ranking for Biomedical Entity Linking](https://mlsanity.com/paper/2407.06292/) (2024)
- [CYCLE: Cross-Year Contrastive Learning in Entity-Linking](https://mlsanity.com/paper/2410.09127/) (2024)
- [Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning](https://mlsanity.com/paper/2410.10144/) (2024)
- [Search-Optimized Quantization in Biomedical Ontology Alignment](https://mlsanity.com/paper/2507.13742/) (2025)
- [Neighborhood-Aware Dual Biomedical Entity Linking](https://mlsanity.com/paper/2608.04144/) (2026)

## Abstract (arXiv)

Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision (tt KRISS) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach can easily incorporate entity descriptions and gold mention labels if available. We conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces tt KRISSBERT, a universal entity linker for four million UMLS entities that attains new state of the art, outperforming prior self-supervised methods by as much as 20 absolute points in accuracy.