# Exploring Partial Knowledge Base Inference in Biomedical Entity Linking

arXiv: https://arxiv.org/abs/2303.10330 · HTML: https://mlsanity.com/paper/2303.10330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 18 Mar 2023.

## Builds on (most cited first, 25 of 25)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (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)
- [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)
- [CODER: Knowledge infused cross-lingual medical term embedding for term normalization](https://mlsanity.com/paper/2011.02947/) (2020)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Improving Biomedical Pretrained Language Models with Knowledge](https://mlsanity.com/paper/2104.10344/) (2021)
- [Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning](https://mlsanity.com/paper/2204.05164/) (2022)
- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [Knowledge-Rich Self-Supervision for Biomedical Entity Linking](https://mlsanity.com/paper/2112.07887/) (2021)
- [HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition](https://mlsanity.com/paper/2008.07347/) (2020)
- [A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization](https://mlsanity.com/paper/1812.06081/) (2018)
- [Fast and Effective Biomedical Entity Linking Using a Dual Encoder](https://mlsanity.com/paper/2103.05028/) (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)
- [BIOS: An Algorithmically Generated Biomedical Knowledge Graph](https://mlsanity.com/paper/2203.09975/) (2022)
- [End-to-end Biomedical Entity Linking with Span-based Dictionary Matching](https://mlsanity.com/paper/2104.10493/) (2021)
- [DiaKG: an Annotated Diabetes Dataset for Medical Knowledge Graph Construction](https://mlsanity.com/paper/2105.15033/) (2021)
- [Biomedical Entity Linking with Contrastive Context Matching](https://mlsanity.com/paper/2106.07583/) (2021)
- [High-throughput relation extraction algorithm development associating knowledge articles and electronic health records](https://mlsanity.com/paper/2009.03506/) (2020)

## Built on by (most cited first, 2 of 2)

- [#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models](https://mlsanity.com/paper/2308.07074/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)

## Abstract (arXiv)

Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED). EL models are trained on corpora labeled by a predefined KB. However, it is a common scenario that only entities within a subset of the KB are precious to stakeholders. We name this scenario partial knowledge base inference: training an EL model with one KB and inferring on the part of it without further training. In this work, we give a detailed definition and evaluation procedures for this practically valuable but significantly understudied scenario and evaluate methods from three representative EL paradigms. We construct partial KB inference benchmarks and witness a catastrophic degradation in EL performance due to dramatically precision drop. Our findings reveal these EL paradigms can not correctly handle unlinkable mentions (NIL), so they are not robust to partial KB inference. We also propose two simple-and-effective redemption methods to combat the NIL issue with little computational overhead. Codes are released at https://github.com/Yuanhy1997/PartialKB-EL.