Reliable Semantic Communication System Enabled by Knowledge Graph

TLDR

Semantic communication tackles bandwidth and power challenges by encoding meaning rather than raw data, and knowledge graphs powered by deep learning enhance semantic representation accuracy and reduce ambiguity. We propose a semantic communication system that leverages a knowledge graph. The system encodes transmitted sentences into knowledge‑graph triplets, ranks them by semantic importance, and dynamically allocates transmission resources to the most critical triplets based on channel quality. Simulations show the system markedly improves reliability in low‑SNR regimes compared to traditional schemes.

Abstract

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.