Supplementary material (Figures) of the paper titled "Restructuring knowledge graphs with conceptual models: implications for machine learning predictions in drug repurposing"

This research explores how restructuring knowledge graphs (KGs) with well-founded conceptual models can improve machine learning (ML) predictions, particularly for drug repurposing. Using OntoUML and the Unified Foundational Ontology, the study applies a FAIRification workflow to enhance data quality. A Graph Neural Network model was trained on both original and restructured KGs, revealing that while classification performance remained similar, the restructured KGs led to more consistent predictions with reduced variability. These findings suggest that conceptual models can enhance the reliability of ML predictions without compromising accuracy, highlighting new directions for future research.