  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.
