Interplay of Semantic Communication and Knowledge Learning
Fei Ni Bingyan Wang Rongpeng Li Zhifeng Zhao Honggang Zhang
In the swiftly advancing realm of communication technologies, Semantic
Communication (SemCom), which emphasizes knowledge understanding and
processing, has emerged as a hot topic. By integrating artificial intelligence
technologies, SemCom facilitates a profound understanding, analysis and
transmission of communication content. In this chapter, we clarify the means of
knowledge learning in SemCom with a particular focus on the utilization of
Knowledge Graphs (KGs). Specifically, we first review existing efforts that
combine SemCom with knowledge learning. Subsequently, we introduce a
KG-enhanced SemCom system, wherein the receiver is carefully calibrated to
leverage knowledge from its static knowledge base for ameliorating the decoding
performance. Contingent upon this framework, we further explore potential
approaches that can empower the system to operate in evolving knowledge base
more effectively. Furthermore, we investigate the possibility of integration
with Large Language Models (LLMs) for data augmentation, offering additional
perspective into the potential implementation means of SemCom. Extensive
numerical results demonstrate that the proposed framework yields superior
performance on top of the KG-enhanced decoding and manifests its versatility
under different scenarios.