The word ontology is everywhere right now. There are "just-in-time ontologies," products with ontology in the name, and major platforms adopting the term in ways that don't always match what it means. This video cuts through the noise and explains, from first principles, what an ontology is, what it isn't, and why it matters in the age of AI agents.
Not New, Just Newly Important
Ontologies have quietly powered ERPs, CRMs, business intelligence tools, and data warehouses for years. What has changed is how much they matter now that AI agents are consuming our data.
Schema Is Structure, Not Meaning
Take a simple customer table with an ID, a name, and a birth date. A schema will happily accept a company as a "customer" with a "birth date" in the year 3000. It checks that a name is a string and a date is a date, but it has no idea what the data actually means.
What a Knowledge Graph Really Is
A knowledge graph is simply a set of triples: subject, predicate, object. It's a very large umbrella covering labeled property graphs queried with Cypher or GQL, as well as RDF triple stores from the semantic web era. Underneath, these systems still store data in rows, columns, and indexes, built on engines like RocksDB or Cassandra. The "graph" is largely for humans. At the machine level, it's a data storage system with retrieval patterns and engineering trade-offs.
So What Is an Ontology?
From first principles, an ontology is the collision of schemas and relationships. Schemas define types, values, and structure. Graphs add relationships, a third degree of freedom. Together, they make disambiguation possible. Is "Ford" a person, a car company, a reference to Brave New World, or a verb for crossing a river?
Before AI agents, fixed API payloads meant we always knew. An agent doesn't. Unless the data carries its meaning with it, the agent has to guess.
What an Ontology Is Not
An ontology is not a product, and it is not a knowledge graph, though the two work hand in hand.
Ontologies in JSON
TrustGraph has supported importing OWL ontologies, but internally they have always been stored as JSON. Most teams don't just want to reuse existing ontologies. They want to build custom ontologies that describe their own business without having to learning OWL or RDF in addition to modeling their knowledge and processes. JSON makes that far more approachable and interoperable.
New TrustGraph features are coming soon to help you build your own ontologies directly from your own data and systems.
For more information:
- GitHub Repository: https://github.com/trustgraph-ai/trustgraph
- Discord Community: https://discord.gg/sQMwkRz5GX
- Website: https://trustgraph.ai
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