  Drug repurposing is more relevant than ever due to drug development's rising
costs and the need to respond to emerging diseases quickly. Knowledge graph
embedding enables drug repurposing using heterogeneous data sources combined
with state-of-the-art machine learning models to predict new drug-disease links
in the knowledge graph. As in many machine learning applications, significant
work is still required to understand the predictive models' behavior. We
propose a structured methodology to understand better machine learning models'
results for drug repurposing, suggesting key elements of the knowledge graph to
improve predictions while saving computational resources. We reduce the
training set of 11.05% and the embedding space by 31.87%, with only a 2%
accuracy reduction, and increase accuracy by 60% on the open ogbl-biokg graph
adding only 1.53% new triples.
