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Word embeddings play a significant role in today's Natural LanguageProcessing tasks and applications. While pre-trained models may be directlyemployed and integrated into existing pipelines, they are often fine-tuned tobetter fit with specific languages or domains. In this paper, we attempt toimprove available embeddings in the uncovered niche of the Italian medicaldomain through the combination of Contrastive Learning (CL) and Knowledge GraphEmbedding (KGE). The main objective is to improve the accuracy of semanticsimilarity between medical terms, which is also used as an evaluation task.Since the Italian language lacks medical texts and controlled vocabularies, wehave developed a specific solution by combining preexisting CL methods(multi-similarity loss, contextualization, dynamic sampling) and theintegration of KGEs, creating a new variant of the loss. Although withouthaving outperformed the state-of-the-art, represented by multilingual models,the obtained results are encouraging, providing a significant leap inperformance compared to the starting model, while using a significantly loweramount of data.

Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language