Predicting Similarities Between Biomedical Ontologies : The UMLs Use-Case

Biomedical ontologies are crucial for organizing domain-specific knowledge, yet traditional alignment methods relying on lexical matching often fail to capture complex semantic relationships. To address this limitation, we propose a novel approach leveraging siamese neural networks and transformerbased models to enhance ontology alignment within the biomedical domain. Our method applies self-supervised contrastive learning to biomedical literature, optimizing the prediction of semantic similarities between concepts in the UMLS Metathesaurus. The results demonstrate that this approach surpasses lexical-based techniques by identifying contextual relationships and uncovering new interconnections among UMLS terminologies. This highlights the potential of our models in improving ontology alignment and enriching biomedical knowledge integration.