  Semantic communication is envisioned as a promising technique to break
through the Shannon limit. However, the existing semantic communication
frameworks do not involve inference and error correction, which limits the
achievable performance. In this paper, in order to tackle this issue, a
cognitive semantic communication framework is proposed by exploiting knowledge
graph. Moreover, a simple, general and interpretable solution for semantic
information detection is developed by exploiting triples as semantic symbols.
It also allows the receiver to correct errors occurring at the symbolic level.
Furthermore, the pre-trained model is fine-tuned to recover semantic
information, which overcomes the drawback that a fixed bit length coding is
used to encode sentences of different lengths. Simulation results on the public
WebNLG corpus show that our proposed system is superior to other benchmark
systems in terms of the data compression rate and the reliability of
communication.
