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 should also be concerned. In this paper, we propose a knowledge enhanced semantic communication framework in which the receiver can more actively utilize the facts in the knowledge base for semantic reasoning and decoding, on the basis of only affecting the parameters rather than the structure of the neural networks at the transmitter side. 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.

翻译：近年来，随着深度学习与自然语言处理技术的快速发展，语义通信已成为通信领域备受关注的研究方向。尽管现有基于深度学习的语义通信方法展现出诸多优势，但仍未充分利用先验知识。同时，现有语义通信方法大多聚焦于发射端语义编码，而我们认为接收端的语义解码能力同样值得关注。本文提出一种知识增强的语义通信框架，该框架中接收端可在仅影响发射端神经网络参数（而非结构）的基础上，更主动地利用知识库中的事实进行语义推理与解码。具体而言，我们设计了一个基于Transformer的知识提取器，用于从接收到的含噪信号中提取相关事实三元组。在WebNLG数据集上的大量仿真结果表明，所提出的接收端在知识图谱增强解码基础上展现出优越性能。