  Semantic communication is a novel communication paradigm which draws
inspiration from human communication focusing on the delivery of the meaning of
a message to the intended users. It has attracted significant interest recently
due to its potential to improve efficiency and reliability of communication,
enhance users' quality-of-experience (QoE), and achieve smoother
cross-protocol/domain communication. Most existing works in semantic
communication focus on identifying and transmitting explicit semantic meaning,
e.g., labels of objects, that can be directly identified from the source
signal. This paper investigates implicit semantic communication in which the
hidden information, e.g., implicit causality and reasoning mechanisms of users,
that cannot be directly observed from the source signal needs to be transported
and delivered to the intended users. We propose a novel implicit semantic
communication (iSC) architecture for representing, communicating, and
interpreting the implicit semantic meaning. In particular, we first propose a
graph-inspired structure to represent implicit meaning of message based on
three key components: entity, relation, and reasoning mechanism. We then
propose a generative adversarial imitation learning-based reasoning mechanism
learning (GAML) solution for the destination user to learn and imitate the
reasoning process of the source user. We prove that, by applying GAML, the
destination user can accurately imitate the reasoning process of the users to
generate reasoning paths that follow the same probability distribution as the
expert paths. Numerical results suggest that our proposed architecture can
achieve accurate implicit meaning interpretation at the destination user.
