  Semantic communication is an emerging paradigm that focuses on understanding
and delivering semantics, or meaning of messages. Most existing semantic
communication solutions define semantic meaning as the meaning of object labels
recognized from a source signal, while ignoring intrinsic information that
cannot be directly observed. Moreover, existing solutions often assume the
recognizable semantic meanings are limited by a pre-defined label database. In
this paper, we propose a novel reasoning-based semantic communication
architecture in which the semantic meaning is represented by a graph-based
knowledge structure in terms of object-entity, relationships, and reasoning
rules. An embedding-based semantic interpretation framework is proposed to
convert the high-dimensional graph-based representation of semantic meaning
into a low-dimensional representation, which is efficient for channel
transmission. We develop a novel inference function-based approach that can
automatically infer hidden information such as missing entities and relations
that cannot be directly observed from the message. Finally, we introduce a
life-long model updating approach in which the receiver can learn from
previously received messages and automatically update the reasoning rules of
users when new unknown semantic entities and relations have been discovered.
Extensive experiments are conducted based on a real-world knowledge database
and numerical results show that our proposed solution achieves 76%
interpretation accuracy of semantic meaning at the receiver, notably when some
entities are missing in the transmitted message.
