Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging

Personalized messaging plays a pivotal role in enhancing communication across domains such as healthcare, education, and professional engagement. This paper introduces a framework that utilizes knowledge graph (KG) to dynamically rephrase written communications by integrating individual- and context-specific data. The knowledge graph represents individuals, locations, and events as critical nodes, linking entities mentioned in messages to their corresponding graph nodes. It retrieves relevant information, such as preferences, professional roles, and cultural norms, which is then combined with the original message and processed through a large language model (LLM) to generate personalized responses. The framework demonstrates notable message acceptance rates across various domains: 42% in healthcare, 53\% in education, and 78% in professional recruitment. By integrating entity linking, event detection, and language modeling, this approach offers a structured and scalable solution for context-aware, audience-specific communication, facilitating advanced applications in diverse fields.