# Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks

arXiv: https://arxiv.org/abs/2305.19979 · HTML: https://mlsanity.com/paper/2305.19979/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 31 May 2023.

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

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [Notes on Noise Contrastive Estimation and Negative Sampling](https://mlsanity.com/paper/1410.8251/) (2014)
- [How to Turn Your Knowledge Graph Embeddings into Generative Models](https://mlsanity.com/paper/2305.15944/) (2023)
- [Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery](https://mlsanity.com/paper/2105.10488/) (2021)
- [SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models](https://mlsanity.com/paper/2109.08002/) (2021)
- [Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning](https://mlsanity.com/paper/2206.10140/) (2022)
- [Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing](https://mlsanity.com/paper/2202.08712/) (2022)
- [Knowledge Graph Completion to Predict Polypharmacy Side Effects](https://mlsanity.com/paper/1810.09227/) (2018)

## Built on by (most cited first, 9 of 9)

- [What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?](https://mlsanity.com/paper/2409.07953/) (2024)
- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs](https://mlsanity.com/paper/2306.03606/) (2023)
- [Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2307.08411/) (2023)
- [The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models](https://mlsanity.com/paper/2409.04103/) (2024)
- [Temporal Smoothness Regularisers for Neural Link Predictors](https://mlsanity.com/paper/2309.09045/) (2023)
- [Type-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models](https://mlsanity.com/paper/2509.26224/) (2025)
- [Canopy: A Heterograph Foundation Model for Metabolic Engineering](https://mlsanity.com/paper/2607.06224/) (2026)
- [Plausibility-Driven Prioritization of Candidate Biomedical Annotations](https://mlsanity.com/paper/2607.20163/) (2026)

## Abstract (arXiv)

Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of whether knowledge graph embeddings have limitations in biomedical settings. This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG, and evaluate their performance and potential downstream uses. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Additionally, we provide interpretable predictions through a rule-based method. We demonstrate that knowledge graph embedding models are applicable in practice by evaluating the best-performing model on four tasks that represent real-life polypharmacy situations. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases. Our code is available at https://github.com/aryopg/biokge.