Drug repositioning, the identification of novel uses of existing therapies, has become an attractive strategy to accelerate drug development. Knowledge graphs (KGs) have emerged as a powerful representation of interconnected data within the biomedical domain. While link prediction on biomedical can ascertain new connections between drugs and diseases, most approaches only state whether two nodes are related. Yet, they fail to explain why two nodes are related. In this project, we introduce an implementation of the semi-parametric Case-Based Reasoning over subgraphs (CBR-SUBG), designed to derive a drug query’s underlying mechanisms by gathering graph patterns of similar nodes. We show that our adaptation outperforms existing KG link prediction models on a drug repositioning task. Furthermore, our findings demonstrate that CBR-SUBG strategy can provide interpretable biological paths as evidence supporting putative repositioning candidates, leading to more informed decisions.