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Knowledge graphs are powerful tools for representing and organising complexbiomedical data. Several knowledge graph embedding algorithms have beenproposed to learn from and complete knowledge graphs. However, a recent studydemonstrates the limited efficacy of these embedding algorithms when applied tobiomedical knowledge graphs, raising the question of whether knowledge graphembeddings have limitations in biomedical settings. This study aims to applystate-of-the-art knowledge graph embedding models in the context of a recentbiomedical knowledge graph, BioKG, and evaluate their performance and potentialdownstream uses. We achieve a three-fold improvement in terms of performancebased on the HITS@10 score over previous work on the same biomedical knowledgegraph. Additionally, we provide interpretable predictions through a rule-basedmethod. We demonstrate that knowledge graph embedding models are applicable inpractice by evaluating the best-performing model on four tasks that representreal-life polypharmacy situations. Results suggest that knowledge learnt fromlarge biomedical knowledge graphs can be transferred to such downstream usecases. Our code is available at https://github.com/aryopg/biokge.

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