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

TL;DR

This paper evaluates the effectiveness of knowledge graph embeddings in biomedical applications, demonstrating significant performance improvements and practical utility in polypharmacy-related tasks.

Contribution

It applies state-of-the-art embedding models to BioKG, achieving notable performance gains and demonstrating their usefulness in real-world biomedical tasks.

Findings

Three-fold improvement in HITS@10 score over previous work

Embeddings enable interpretable rule-based predictions

Models transfer knowledge effectively to polypharmacy tasks

Abstract

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.