This research presents a framework that combines Knowledge Graphs and Large Language Models to create contextually aware, personalized messaging across multiple domains.

Key findings:

• Integration of individual and context-specific data through knowledge graphs improves message relevance
• Healthcare applications showed 42% message acceptance rate
• Education applications achieved 53% message acceptance rate
• Framework dynamically rephrases communications by linking message entities to graph nodes

Medical Impact: The framework significantly enhances healthcare communications by tailoring messages to patient contexts, medical histories, and treatment plans, potentially improving adherence and engagement with medical advice.

Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging