Context: In recent years, the field of biomedicine has been experiencing a huge growth of interest, particularly in the application of knowledge mining algorithms. The extraction of knowledge from the scientific literature is valuable for assisting professionals in making well-informed decisions supported by relevant documents. This thesis discusses a novel approach for the Named Entity Disambiguation (NED) task, applied to the biomedical field. The proposed approach combines pre-trained language models and graph technologies for the NED task. It is worth noting that this methodology is not limited to the biomedical field, but it could be applied to various domains. However, the biomedical domain is employed as a case study, since it is one of the most complex due to the vast number of entities and a lack of sufficient clarity in the available literature.