Knowledge Graph-Based Explainable and Generalized Zero-Shot Semantic Communications
This work addresses the problem of poor generalization and high overhead in semantic communications for dynamic or resource-constrained environments, offering an incremental improvement through knowledge graph integration.
The paper tackles the lack of interpretability and generalization in data-driven semantic communication by proposing a knowledge graph-enhanced zero-shot semantic communication network, which significantly outperforms existing frameworks in classifying unseen categories on the APY datasets across various SNR levels.
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.