In healthcare, being able to efficiently manipulate and compare concepts in ontologies is crucial to enable semantic interoperability of clinical data. Most ontology-based similarity functions return a single score with little actionable justification, limiting their trust and use in clinical workflows. In this paper, we focus on the explainability of similarity computations rather than on defining yet another similarity measure. We introduce an explainable similarity framework that returns a score together with a small set of named pivots and readable paths from concept to pivot. The contribution of each pivot is a combination of intrinsic topological specificity and path-length proximity, aggregated into a similarity score. Qualitative cases derived from SNOMED CT show specific pivots for clinically close pairs and broad pivots for distant ones. An internal AUC of 0.998 matches classical measures while providing path-based explanations; explainability metrics confirm shorter paths, deeper pivots, and higher pivot impact for close pairs.