cognee/docs/kr/core-concepts/further-concepts/ontologies.md

Ontologies

Enrich your knowledge graph with external vocabularies

What is an ontology in Cognee?

An ontology is an optional RDF/OWL file you can provide to Cognee. It acts as a reference vocabulary, making sure that entity types ("classes") and entity mentions ("individuals") extracted from your data are linked to canonical, well-defined concepts.

How it works

• You pass ontology_file_path="my_ontology.owl" when running Cognify.
• Cognee parses the file with RDFLib and loads its classes and relationships.
• During graph extraction, entities and types are checked against the ontology:
  • If a match is found, the node is marked ontology_valid=True.
  • Parent classes and object-property links from the ontology are attached as extra edges.
• If no ontology is provided, extraction still works, just without validation or enrichment.

Why use an ontology

• Consistency: standardize how entities and types are represented
• Enrichment: bring in inherited relationships from a domain schema
• Control: align Cognee's graph with existing enterprise or scientific vocabularies

Where to get ontologies

Ontologies are an art and science on their own. Cognee works best with manually curated, focused ontologies that fit your dataset. The simplest way to start is to create a small ontology yourself — just a few classes and relationships that match the entities you expect.

Public resources like Wikidata or DBpedia define millions of classes and entities, which makes them too big to use directly in Cognee. If you are not creating an ontology from scratch, you can start from a public one — but always work with a subset, not the full ontology:

• Select only the pieces you need (specific classes, properties, or individuals)
• Save the subset in a format Cognee can parse with rdflib
• If needed, enrich the subset manually by adding extra classes or relationships relevant to your domain
• Keep it small and relevant so matching stays precise and performance remains fast

- **General vocabularies**: schema.org, Dublin Core Terms (DC/Terms), SKOS, PROV-O, FOAF - **Knowledge graph backbones**: DBpedia Ontology, Wikidata (Wikibase RDF ontology) - **Domain examples**: * Healthcare: SNOMED CT (licensed), ICD, UMLS, MeSH, HL7/FHIR RDF * Finance: FIBO (Financial Industry Business Ontology) * Geo/IoT: GeoSPARQL, SOSA/SSN, GeoNames * Units: QUDT Every public ontology is **too broad to ingest wholesale**. Creating a subset is what makes them usable in Cognee:

* Improves matching precision (fewer false matches when mapping LLM output)
* Keeps performance acceptable (smaller graphs → faster resolution)
* Lets you curate only the relevant parts of a domain

Different communities provide different ways to extract subsets (e.g., "slims" in OBO ontologies, WDumper for Wikidata, module extraction in Protégé). The details vary, but the general principle is the same:

1. Pick the terms (classes or properties) you care about
2. Extract those terms plus their immediate context (e.g. parent classes, related properties)
3. Save the result in an `rdflib`-readable RDF format

Supported formats

Any format RDFLib can parse:

• RDF/XML (.owl, .rdf)
• Turtle (.ttl)
• N-Triples, JSON-LD, and others

Practical example

Once you have your subset file, integrating it into Cognee is simple:

import cognee

await cognee.cognify(
    datasets=["my_dataset"],
    ontology_file_path="subset.owl",  # your curated subset here
)

For more detailed examples of working with ontologies in Cognee, check out the demo scripts in the repository:

• Basic ontology demo - Shows fundamental ontology integration
• Advanced ontology demo - Demonstrates more complex ontology workflows

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To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt