The goal of this paper is to promote the idea that including semantic and\ngoal-oriented aspects in future 6G networks can produce a significant leap\nforward in terms of system effectiveness and sustainability. Semantic\ncommunication goes beyond the common Shannon paradigm of guaranteeing the\ncorrect reception of each single transmitted packet, irrespective of the\nmeaning conveyed by the packet. The idea is that, whenever communication occurs\nto convey meaning or to accomplish a goal, what really matters is the impact\nthat the correct reception/interpretation of a packet is going to have on the\ngoal accomplishment. Focusing on semantic and goal-oriented aspects, and\npossibly combining them, helps to identify the relevant information, i.e. the\ninformation strictly necessary to recover the meaning intended by the\ntransmitter or to accomplish a goal. Combining knowledge representation and\nreasoning tools with machine learning algorithms paves the way to build\nsemantic learning strategies enabling current machine learning algorithms to\nachieve better interpretation capabilities and contrast adversarial attacks. 6G\nsemantic networks can bring semantic learning mechanisms at the edge of the\nnetwork and, at the same time, semantic learning can help 6G networks to\nimprove their efficiency and sustainability.\n