If you've ever used Google to search for a famous person and seen that box on the right side with their birthdate, spouse, and movies—you've used a Knowledge Graph.
At its simplest, a Knowledge Graph is a way of organizing data that highlights relationships between things, rather than just listing them in rows and columns like a spreadsheet.
Nodes and Edges
Knowledge Graphs are built from two basic building blocks:
- Nodes (The "Things")These represent entities: people, places, companies, concepts, or documents. For example: "Jake Moroshek" or "BuildAI".
- Edges (The "Relationships")These describe how nodes are connected. For example: "FOUNDED" or "LOCATED_IN".
A simple graph: Node -> Edge -> Node
Why Spreadsheets Fail
Spreadsheets are great for lists. But the real world is messy and interconnected. If you try to map a complex supply chain or a regulatory framework in Excel, you end up with a nightmare of VLOOKUPs and pivot tables.
Knowledge Graphs embrace this complexity. They allow you to ask questions like:
"Show me all suppliers who provide components for Project X that are also located in regions with high climate risk."
This is the power of GraphRAG. It uses this structured understanding to give AI the context it needs to answer your hardest questions accurately.