KGRAG-SC: Knowledge Graph RAG-Assisted Semantic Communication

Abstract

The state-of-the-art semantic communication (SC) schemes typically rely on end-to-end deep learning frameworks that lack interpretability and struggle with robust semantic selection and reconstruction under noisy conditions. To address this issue, this paper presents KGRAG-SC, a knowledge graph-assisted SC framework that leverages retrieval-augmented generation principles. KGRAG-SC employs a multi-dimensional knowledge graph, enabling efficient semantic extraction through community-guided entity linking and GraphRAG-assisted processing. The transmitter constructs minimal connected subgraphs that capture essential semantic relationships and transmits only compact entity indices rather than full text or semantic triples. An importance-aware adaptive transmission strategy provides unequal error protection based on structural centrality metrics, prioritizing critical semantic elements under adverse channel conditions. At the receiver, large language models perform knowledge-driven text reconstruction using the shared knowledge graph as structured context, ensuring robust semantic recovery even with partial information loss. Experimental results demonstrate that KGRAG-SC achieves superior semantic fidelity in low Signal-to-Noise Ratio (SNR) conditions while significantly reducing transmission overhead compared to traditional communication methods, highlighting the effectiveness of integrating structured knowledge representation with generative language models for SC systems.