  Semantic communication is envisioned as a promising technique to break
through the Shannon limit. However, semantic inference and semantic error
correction have not been well studied. Moreover, error correction methods of
existing semantic communication frameworks are inexplicable and inflexible,
which limits the achievable performance. In this paper, to tackle this issue, a
knowledge graph is exploited to develop semantic communication systems. Two
cognitive semantic communication frameworks are proposed for the single-user
and multiple-user communication scenarios. Moreover, a simple, general, and
interpretable semantic alignment algorithm for semantic information detection
is proposed. Furthermore, an effective semantic correction algorithm is
proposed by mining the inference rule from the knowledge graph. Additionally,
the pre-trained model is fine-tuned to recover semantic information. For the
multi-user cognitive semantic communication system, a message recovery
algorithm is proposed to distinguish messages of different users by matching
the knowledge level between the source and the destination. Extensive
simulation results conducted on a public dataset demonstrate that our proposed
single-user and multi-user cognitive semantic communication systems are
superior to benchmark communication systems in terms of the data compression
rate and communication reliability. Finally, we present realistic single-user
and multi-user cognitive semantic communication systems results by building a
software-defined radio prototype system.
