In the modern labor market, skill mismatch is one of the most significant challenges both employers and candidates face. Traditional recruitment and training systems often struggle to effectively match candidates' skills with job roles and the necessary training programs. Leveraging advanced technologies like recommender systems powered by ontological models presents a promising solution to this problem. This approach not only facilitates the matching of job roles with candidates but also supports personalized skill development paths by identifying gaps and recommending training or certification programs. This solution will also provide support for professionals to reorient themselves towards new professions. In this paper, we explore how an ontological model can be used within a recommender system for skills assessment and personalized job or training recommendations.