In the current information era, where the amount of available data is growing exponentially, recommendation systems have become essential tools for personalizing services and enhancing user experience across a wide range of applications, from e-commerce to education and employment. These systems face significant challenges, such as the accuracy of recommendations under "cold start" conditions and the scarcity of interactions between users and recommended items.

In this context, the use of ontologies has emerged as a promising approach to improving the quality and relevance of recommendations. By providing a formal and structured representation of knowledge in a specific domain, they enable the precise and detailed modeling of relevant elements for recommendation.

Social networks have become an important source of information, with professional networks like LinkedIn serving as a prime example, where updated information about individuals is available in relation to their professional profiles and areas of expertise. To develop a tool that helps professionals enhance their skills, a recommendation system for life long learning courses was developed, based on ontology and Machine Learning, to improve professional competencies.

Initially, an ontology was proposed to model work performance sectors and areas of knowledge based on data extracted from LinkedIn. The ontology is updated through events using profiled data obtained from professional records on social networks. Additionally, by leveraging Machine Learning techniques, it clusters entities to make predictions for new data.

The main objectives of this work are, on one hand, to incorporate other professional social networks and networking platforms such as Xing, BeBee, and Indeed as data sources, allowing information to be extracted from multiple sources to enrich the database and, consequently, the ontology. On the other hand, the goal is to design and develop a user interface that enables interaction and validation of the system developed so far, allowing end users to make evidence-based decisions when selecting training courses.

At the methodological level, the databases used are primarily Neo4J and MongoDB, both of which are non-relational databases that naturally facilitate the application of Machine Learning algorithms.

The user interface allows a user to input their profile to receive recommendations in a specific area of knowledge, along with an evaluation module for these recommendations, providing relevant information. A course selection based on market data can enhance not only the training itself but also employability.

Both the incorporation of new data sources and the development of the user interface have helped validate the proposed ontology for the professional context and provided an innovative system that assists in decision-making for lifelong learning.

Keywords: Ontology, Life long learning, Machine learning, Social Networks.