This research aims to realize a knowledge-based system recognizing teenagers’ behaviours in the lifestyle domain. Since various heterogeneous data sources are involved in this recognition process (e.g. wearable sensors, etc.), it is essential to face the harmonization of the data produced from these disparate sources and expressed under the form of both structured and unstructured content. The herein proposed approach leverages a common semantic reference model which represents the individual as a whole, permitting classification and integration of behavioural and contextual information. In this regard, an automatic semantic enrichment of the data produced by the involved sources is carried out, thus allowing to support a behaviour recognition process handled in a two-phase process (real-time and long-term detection and analysis of behaviours and trends).