Imagine a world where wireless devices communicate not just data, but pure meaning. This is the vision of semantic communication, a paradigm shift that prioritizes the transmission of knowledge over mere bits and bytes. Recent research explores how knowledge graphs, combined with the power of AI, could revolutionize how we share information wirelessly. Knowledge graphs, structured representations of interconnected concepts, offer a powerful way to organize and transmit knowledge. By encoding information as relationships between entities within these graphs, semantic communication systems can achieve remarkably efficient compression. The research introduces an innovative approach using Large Language Models (LLMs) and Graph Neural Networks (GNNs) to represent and process knowledge. LLMs extract initial features from the data, and GNNs incorporate the knowledge graph's topology to create compact semantic embeddings. These embeddings, representing nodes and their relationships, are then transmitted. Impressively, the receiver can reconstruct the entire knowledge graph from just the node embeddings, demonstrating significant compression gains over traditional methods like Huffman encoding. This approach not only compresses information efficiently but also enhances robustness against noise, especially in low signal-to-noise ratio (SNR) environments where traditional methods struggle. This advancement in semantic communication holds the promise of more efficient and resilient wireless systems, particularly beneficial for applications like Internet of Things (IoT) where numerous devices communicate complex data. Challenges remain in scaling this technology for broader applications and refining the decoding process for even higher accuracy. However, this research marks a significant step toward unlocking the potential of semantic communication, paving the way for future networks that truly understand the meaning of data.