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

本文介绍了将语义和目标导向应用于未来的 6G 网络中，将有助于提高系统的效率和可持续性，同时结合知识表示和推理工具以及机器学习算法，可以建立语义学习策略从而实现更好的解释能力，对抗敌对攻击。