Dev Conference by Neo4j
Session Track: AI
Session Time:
Session description
This session will delve into query generation with large language models (LLMs) in the realm of complex knowledge graphs. Our approach targets users demanding precision in results, seamlessly translating natural language utterance into Cypher queries through the integration of multi-agent reasoning, schema-based context, and descriptions. We’ll unveil how our approach mirrors human cognitive processes, improving the querying experience on large knowledge graphs, drawing from real-world use cases. The examples presented highlight the system’s ability to deliver accurate and efficient results. The combination of LLMs and expert-inspired query methodologies opens new avenues for navigating the intricate landscape of knowledge querying.
Lead Machine Learning Engineer, GraphAware
As Lead Machine Learning Engineer at GraphAware, Fabio has dedicated himself to driving innovation at the intersection of science and technology. With over 15 years of experience in software engineering, his career has spanned multiple fields, including neuroscience and operational oceanography. He has collaborated with researchers to deliver practical insights that drive real-world impact. By designing support infrastructures that bridge the gap between academia and industry, Fabio has helped scientists turn their research into tangible products, ultimately making a difference in people's lives.
