Webinar
How NASA is Using Graph Technology and LLMs to Build a People Knowledge Graph
Managing people data at an organization like NASA comes with unique challenges. Traditional databases make it hard to analyze relationships across projects, skills, and employees. To solve this, NASA’s People Analytics team built a People Knowledge Graph using Memgraph and Large Language Models (LLMs).
Hosted on a secure AWS environment, the system helps identify subject matter experts, support quick leadership reporting, and uncover collaboration opportunities across the agency. In this session, the team shares how they built the graph, used LLMs for skill extraction, and enabled natural language querying with a RAG-based chatbot.
Join David Meza, Madison Ostermann, and Katherine Knott from NASA as they walk through their technical approach and the insights gained along the way.
What’s in the Webinar?
What You’ll Learn
Hosted on a secure AWS environment, the system helps identify subject matter experts, support quick leadership reporting, and uncover collaboration opportunities across the agency. In this session, the team shares how they built the graph, used LLMs for skill extraction, and enabled natural language querying with a RAG-based chatbot.
Join David Meza, Madison Ostermann, and Katherine Knott from NASA as they walk through their technical approach and the insights gained along the way.
What’s in the Webinar?
- Why traditional databases limit workforce analytics
- How NASA built a skills graph using Memgraph and LLMs
- Live demo: Querying a skills knowledge graph using LLM-extracted data for insights
- A look at NASA’s new GraphRAG chatbot interface powered by Memgraph
What You’ll Learn
- What to consider when building secure, cloud-based graph systems
- How to ingest data securely from AWS S3 into Memgraph using Python and GQLAlchemy
- How LLMs extract skills from resumes and project text
- Why Memgraph was chosen over Neo4j for this government-scale deployment
