In recent years, with the rapid development of deep learning and natural
language processing technologies, semantic communication has become a topic of
great interest in the field of communication. Although existing deep learning
based semantic communication approaches have shown many advantages, they still
do not make sufficient use of prior knowledge. Moreover, most existing semantic
communication methods focus on the semantic encoding at the transmitter side,
while we believe that the semantic decoding capability of the receiver side
should also be concerned. In this paper, we propose a knowledge enhanced
semantic communication framework in which the receiver can more actively
utilize the prior knowledge in the knowledge base for semantic reasoning and
decoding, without extra modifications to the neural network structure of the
transmitter. Specifically, we design a transformer-based knowledge extractor to
find relevant factual triples for the received noisy signal. Extensive
simulation results on the WebNLG dataset demonstrate that the proposed receiver
yields superior performance on top of the knowledge graph enhanced decoding.

本文中，我们提出了一种知识增强语义通信框架，其中接收方可以更积极地利用知识库中的先前知识进行语义推理和解码，无需对发射器的神经网络结构进行额外修改。