A knowledge graph is a persistent, structured network of entities and the relationships between them, built to represent a broad domain, such as an organization's data assets or business concepts, in a form that both people and systems can query.
How a Knowledge Graph Is Structured
A knowledge graph is made of nodes, which represent entities such as a table, a business metric, a customer, or a regulation, and edges, which represent the relationships between them, such as "derives from," "owned by," or "subject to." Unlike a flat list or table, a graph structure lets you traverse those relationships, for example following a metric back through every transformation that produced it, or finding every dataset that touches a particular regulation.
Knowledge graphs are built to be broad and durable. They aim to represent an entire domain, not a single question, and they persist over time as the authoritative structure that other systems query against. This is what makes them useful as a foundation for search, for recommendation, and increasingly for AI systems that need a reliable, machine readable map of how things in an organization relate to one another.
It is worth being precise here, because the term is often used loosely alongside a related but distinct concept. A context graph is a dynamically assembled, situational slice pulled from a knowledge graph to answer one specific question in the moment, narrow by design and temporary. The knowledge graph is the broad, persistent structure underneath; the context graph is what gets pulled from it on demand.
How a Knowledge Graph Relates to Adjacent Terms
A knowledge graph is frequently confused with a data catalog. A catalog is an inventory, a list of data assets with descriptions and owners attached. A knowledge graph goes further by capturing the relationships between those assets, and between assets and business concepts, not just the assets themselves. A catalog tells you a table exists. A knowledge graph tells you what feeds it, what depends on it, and what business meaning it carries.
It also differs from a context graph, as described above: broad and persistent versus narrow and situational.
What a Knowledge Graph Needs Underneath It
An AI system is only as reliable as the knowledge graph it draws from. A graph built by scanning warehouse metadata or a catalog's self reported descriptions inherits every gap and inaccuracy in that documentation, and it has no way to see the transformation logic that actually explains what a field means or where a value came from. Building a knowledge graph through source code analysis, reading the actual SQL, Python, Java, dbt, Spark, and ORM code across an organization's application layer, means the relationships in the graph, and the context graphs assembled from it for a given query, trace back to where data genuinely originates rather than just where it was last documented.
For an AI or ML leader, that is the difference between an agent that can explain its answer with a verifiable chain back to source code and one that is guessing from stale metadata.
Related Glossary Terms
Frequently Asked Questions
What is the difference between a knowledge graph and a context graph?
A knowledge graph is the broad, persistent structure that represents an entire domain, such as all of a company's data assets and how they relate. A context graph is a narrower, temporary slice pulled from that knowledge graph to answer one specific question, assembled dynamically at query time rather than stored permanently. The knowledge graph is the map; the context graph is the route pulled from it for one trip.
Is a knowledge graph the same as a data catalog?
No. A data catalog is an inventory that lists data assets with descriptions and owners attached. A knowledge graph captures the relationships between those assets and business concepts as a connected structure, which lets you traverse from one entity to everything it depends on or feeds. A catalog tells you what exists. A knowledge graph tells you how it connects.
Why does a knowledge graph matter for AI agents?
An AI agent that answers questions about company data is only as accurate as the graph it queries. If that graph is built from incomplete or self reported metadata, the agent inherits those gaps. A knowledge graph built from source code analysis traces relationships back to where data actually originates, giving an agent a more reliable foundation to reason from and cite.
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Give Your AI Agents a Graph They Can Trust
Request a demo to see a knowledge graph built from source code analysis, not self reported metadata.
Give Your AI Agents a Graph They Can Trust
Request a demo to see a knowledge graph built from source code analysis, not self reported metadata.
Give Your AI Agents a Graph They Can Trust
Request a demo to see a knowledge graph built from source code analysis, not self reported metadata.
