  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.
