Abstract Recent technologies advancements promise to change our lives dramatically in the near future. A new different living society is progressively emerging, witnessed from the conception of novel digital ecosystems, where humans are expected to share their own spaces and habits with machines. Humanoid robots are more and more being developed and provided with enriched functionalities; however, they are still lacking in many ways. One important goal in this sense is to enrich their cognitive capabilities, to make them more “intelligent” in order to better support humans in both daily and special activities. The goal of this research is to set a step in bridging the gap between symbolic AI and connectionist approaches in the context of knowledge acquisition and conceptualization. Hence, we present a combined approach based on semantics and machine learning techniques for improving robots cognitive capabilities. This is part of a wider framework that covers several aspects of knowledge management, from representation and conceptualization, to acquisition, sharing and interaction with humans. Our focus in this work is in particular on the development and implementation of techniques for knowledge acquisition. Such techniques are discussed and validated through experiments, carried out on a real robotic platform, showing the effectiveness of our approach. The results obtained confirmed that the combination of the approaches gives superior performance with respect to when they are considered individually.