# Comparison of biomedical relationship extraction methods and models for knowledge graph creation

arXiv: https://arxiv.org/abs/2201.01647 · HTML: https://mlsanity.com/paper/2201.01647/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 80 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [SciFive: a text-to-text transformer model for biomedical literature](https://mlsanity.com/paper/2106.03598/) (2021)
- [GMNN: Graph Markov Neural Networks](https://mlsanity.com/paper/1905.06214/) (2019)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers](https://mlsanity.com/paper/2001.08904/) (2020)
- [Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models](https://mlsanity.com/paper/1802.01255/) (2018)
- [MASK: A flexible framework to facilitate de-identification of clinical texts](https://mlsanity.com/paper/2005.11687/) (2020)
- [Biomedical Information Extraction for Disease Gene Prioritization](https://mlsanity.com/paper/2011.05188/) (2020)
- [Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017](https://mlsanity.com/paper/1905.11716/) (2019)

## Built on by (most cited first, 6 of 6)

- [Fact Finder -- Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs](https://mlsanity.com/paper/2408.03010/) (2024)
- [From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts](https://mlsanity.com/paper/2305.04928/) (2023)
- [Applying BioBERT to Extract Germline Gene-Disease Associations for Building a Knowledge Graph from the Biomedical Literature](https://mlsanity.com/paper/2309.13061/) (2023)
- [High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models](https://mlsanity.com/paper/2312.08274/) (2023)
- [A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domain](https://mlsanity.com/paper/2411.12752/) (2024)
- [Hidden Entity Detection from GitHub Leveraging Large Language Models](https://mlsanity.com/paper/2501.04455/) (2025)

## Abstract (arXiv)

Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, accessed, and validated. Knowledge graphs can provide such a framework for semantic knowledge representation from literature. However, in order to build a knowledge graph, it is necessary to extract knowledge as relationships between biomedical entities and normalize both entities and relationship types. In this paper, we present and compare few rule-based and machine learning-based (Naive Bayes, Random Forests as examples of traditional machine learning methods and DistilBERT, PubMedBERT, T5 and SciFive-based models as examples of modern deep learning transformers) methods for scalable relationship extraction from biomedical literature, and for the integration into the knowledge graphs. We examine how resilient are these various methods to unbalanced and fairly small datasets. Our experiments show that transformer-based models handle well both small (due to pre-training on a large dataset) and unbalanced datasets. The best performing model was the PubMedBERT-based model fine-tuned on balanced data, with a reported F1-score of 0.92. DistilBERT-based model followed with F1-score of 0.89, performing faster and with lower resource requirements. BERT-based models performed better then T5-based generative models.