论文标题
知识图驱动的认知语义通信系统
Cognitive Semantic Communication Systems Driven by Knowledge Graph
论文作者
论文摘要
语义沟通被认为是突破香农限制的一种有前途的技术。但是，现有的语义通信框架不涉及推理和错误校正，这限制了可实现的性能。在本文中，为了解决此问题，通过利用知识图提出了认知语义交流框架。此外，通过将三元组作为语义符号来开发出一种简单，一般和可解释的语义信息检测解决方案。它还允许接收器纠正在符号级别上发生的错误。此外，对预训练的模型进行了微调以恢复语义信息，这克服了固定的位长度编码用于编码不同长度的句子的缺点。公共WebNLG语料库上的仿真结果表明，就数据压缩率和通信的可靠性而言，我们提出的系统优于其他基准系统。
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.
