# Medical Entity Linking using Triplet Network

arXiv: https://arxiv.org/abs/2012.11164 · HTML: https://mlsanity.com/paper/2012.11164/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking](https://mlsanity.com/paper/2306.12245/) (2023)
- [BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification](https://mlsanity.com/paper/2104.01782/) (2021)
- [Biomedical Entity Linking with Contrastive Context Matching](https://mlsanity.com/paper/2106.07583/) (2021)
- [KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph](https://mlsanity.com/paper/2103.08893/) (2021)
- [Biomedical Interpretable Entity Representations](https://mlsanity.com/paper/2106.09502/) (2021)
- [GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction](https://mlsanity.com/paper/2112.03002/) (2021)
- [UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition](https://mlsanity.com/paper/2307.11170/) (2023)
- [A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization](https://mlsanity.com/paper/2101.09101/) (2021)
- [Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph](https://mlsanity.com/paper/2106.07549/) (2021)
- [Class Granularity: How richly does your knowledge graph represent the real world?](https://mlsanity.com/paper/2411.06385/) (2024)

## Abstract (arXiv)

Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.