  Word embeddings play a significant role in today's Natural Language
Processing tasks and applications. While pre-trained models may be directly
employed and integrated into existing pipelines, they are often fine-tuned to
better fit with specific languages or domains. In this paper, we attempt to
improve available embeddings in the uncovered niche of the Italian medical
domain through the combination of Contrastive Learning (CL) and Knowledge Graph
Embedding (KGE). The main objective is to improve the accuracy of semantic
similarity between medical terms, which is also used as an evaluation task.
Since the Italian language lacks medical texts and controlled vocabularies, we
have developed a specific solution by combining preexisting CL methods
(multi-similarity loss, contextualization, dynamic sampling) and the
integration of KGEs, creating a new variant of the loss. Although without
having outperformed the state-of-the-art, represented by multilingual models,
the obtained results are encouraging, providing a significant leap in
performance compared to the starting model, while using a significantly lower
amount of data.
