Semantic Communication Enhanced by Knowledge Graph Representation Learning

• Published in: arXiv.org
• Main Authors: Hello, Nour, Paolo Di Lorenzo, Emilio Calvanese Strinati
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.07.2024
• Subjects: Coders; Graph neural networks; Graph representations; Graph theory; Graphical representations; Graphs; Intelligent agents; Knowledge representation; Large language models; Machine learning; Nodes; Semantics; Wireless communications
• ISSN: 2331-8422

This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic communications. The proposed approach leverages semantic and pragmatic aspects, incorporating recent advances on large language models (LLMs) to achieve compact representations of knowledge to be processed and exchanged between intelligent agents. This is accomplished by using the cascade of LLMs and graph neural networks (GNNs) as semantic encoders, where information to be shared is selected to be meaningful at the receiver. The embedding vectors produced by the proposed semantic encoder represent information in the form of triplets: nodes (semantic concepts entities), edges(relations between concepts), nodes. Thus, semantic information is associated with the representation of relationships among elements in the space of semantic concept abstractions. In this paper, we investigate the potential of achieving high compression rates in communication by incorporating relations that link elements within graph embeddings. We propose sending semantic symbols solely equivalent to node embeddings through the wireless channel and inferring the complete knowledge graph at the receiver. Numerical simulations illustrate the effectiveness of leveraging knowledge graphs to semantically compress and transmit information.