Abstract: Systems and methods for aligning ontologies, such as a medical or related ontologies, are disclosed. Initially, ontology specifications are received, such as ontologies comprising a root node and a plurality of child nodes. Each node is assigned at least one synthetic identifier corresponding to its path(s) to the root node. In some cases, nodes may be clustered using one or more clustering algorithms. A translation model is pre-trained by applying one or more masked language models to the ontologies and the synthetic identifiers. Subsequently, each ontology is augmented by identifying nodes in different ontologies that match and assigning label and/or other details across different ontologies. The translation model can then be fine-tuned using the augmented data. The fine-tuned translation model is then used to identify corresponding nodes in target ontologies in response to translation requests.