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How to Build Your First Knowledge Graph from Documents
Most organizations already have the raw material for a useful knowledge graph. This information is found in PDFs, reports, policies, contracts, manuals, articles, and internal notes. The challenge is not a lack of data, but that it is locked inside regular text. A knowledge graph converts unstructured text into a network of connected facts, such as people, companies, products, places, events, and their relationships.
Modern tools like Neo4j and Microsoft GraphRAG use a similar process: break documents into smaller pieces, extract entities and relationships, merge and summarize related information, and then use the graph for search, analysis, or question answering.
Your first knowledge graph should not try to capture everything at once. Instead, focus on building a model that answers a few key questions well. This approach helps you create something useful quickly, rather than getting stuck in endless prototypes. Neo4j recommends starting with your use cases and questions, and experts agree that your schema, identity, and query needs should guide the graph from the start.
Start With the Questions Your Documents Should Answer
Before you start processing files, define what success means for your project. For example, do you want to answer…
