Knowledge Graph-Based Explainable and Generalized Zero-Shot Semantic Communications

TL;DR

This paper introduces a knowledge graph-enhanced zero-shot semantic communication network that improves interpretability, generalization to unseen data, and classification efficiency by leveraging structured semantic information and reasoning capabilities.

Contribution

It proposes a novel KGZS-SC network that uses a knowledge graph for structured semantic representation and zero-shot learning for unseen data classification, enhancing generalization and efficiency.

Findings

Outperforms existing frameworks in classifying unseen categories.

Demonstrates robustness across various SNR levels.

Reduces communication overhead with selective semantic transmission.

Abstract

Data-driven semantic communication is based on superficial statistical patterns, thereby lacking interpretability and generalization, especially for applications with the presence of unseen data. To address these challenges, we propose a novel knowledge graph-enhanced zero-shot semantic communication (KGZS-SC) network. Guided by the structured semantic information from a knowledge graph-based semantic knowledge base (KG-SKB), our scheme provides generalized semantic representations and enables reasoning for unseen cases. Specifically, the KG-SKB aligns the semantic features in a shared category semantics embedding space and enhances the generalization ability of the transmitter through aligned semantic features, thus reducing communication overhead by selectively transmitting compact visual semantics. At the receiver, zero-shot learning (ZSL) is leveraged to enable direct classification for unseen cases without the demand for retraining or additional computational overhead, thereby enhancing the adaptability and efficiency of the classification process in dynamic or resource-constrained environments. The simulation results conducted on the APY datasets show that the proposed KGZS-SC network exhibits robust generalization and significantly outperforms existing SC frameworks in classifying unseen categories across a range of SNR levels.