Semantic Communication Enhanced by Knowledge Graph Representation Learning
This work addresses the challenge of efficient communication for intelligent agents by enhancing semantic compression, though it appears incremental as it builds on existing LLM and GNN techniques.
The paper tackles the problem of achieving high compression rates in semantic communications by representing semantic knowledge as graphs, using a cascade of large language models and graph neural networks as semantic encoders to transmit only node embeddings and infer the complete knowledge graph at the receiver, with numerical simulations demonstrating effectiveness.
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.