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.

翻译：语义通信被视为突破香农极限的一项有前景的技术。然而，语义推理与语义纠错尚未得到充分研究。此外，现有语义通信框架中的纠错方法缺乏可解释性和灵活性，限制了其性能表现。为解决这一问题，本文利用知识图谱开发语义通信系统，针对单用户和多用户通信场景提出了两种认知语义通信框架。同时，提出了一种简单、通用且可解释的语义对齐算法用于语义信息检测。进一步地，通过从知识图谱中挖掘推理规则，提出了一种有效的语义校正算法。此外，通过对预训练模型进行微调以恢复语义信息。针对多用户认知语义通信系统，提出了一种通过匹配信源与信宿知识层级来区分不同用户信息的消息恢复算法。基于公开数据集的仿真结果表明，本文提出的单用户与多用户认知语义通信系统在数据压缩率和通信可靠性方面均优于基准通信系统。最后，通过构建软件定义无线电原型系统，展示了真实场景下的单用户与多用户认知语义通信系统实验结果。