Building Contextual Knowledge Graphs to Adapt, Evaluate, and Leverage LLMs

Session Track: AI Engineering

Session Time: 12:30 pm - 01:00 pm November 6

Session description

In this session, I’ll share how I built contextual knowledge graphs by combining diverse datasets like Geonames, OpenStreetMap, Yelp, and Wikidata. These graphs were designed to enrich LLMs with real-world context, enabling more relevant and grounded outputs.​ I’ll walk you through the process of modeling user behavior and spatial semantics, and how I used Neo4j to prototype, query, and scale these graphs. Along the way, I’ll highlight lessons learned in schema design, Cypher optimization, and integrating graph-based context into LLM workflows.​ You’ll learn practical techniques to adapt LLMs using graph-driven context, evaluate their outputs with graph-based signals, and leverage Neo4j to bridge structured knowledge and generative AI. Whether you’re building AI applications or exploring graph-powered personalization, this session will provide actionable insights to enhance your projects.