Ontology engineering
An ontology is the agreed vocabulary of your domain: what things exist, how they relate, and which rules hold. Done well, it is the contract between your data teams, your applications and your AI. We build ontologies that are precise enough for machines and plain enough for people.
When this is the right service
- "Customer", "account" and "client" mean different things in different departments.
- A new integration starts with weeks of arguing about field names.
- Classification of products, documents or cases is inconsistent and nobody owns the list.
- You need to publish or exchange data using industry standards (FIBO, schema.org, SNOMED, ISO vocabularies).
What we do
- 1
Scoping and competency questions
We start from the questions the ontology must answer, not from an abstract model. This keeps it small and useful.
- 2
Modeling
Classes, properties, constraints and definitions in OWL or RDFS, with SKOS for controlled vocabularies, reusing standard ontologies where they fit.
- 3
Validation
SHACL shapes and test data so every change is checked automatically, the same way code is.
- 4
Documentation and governance
Human readable documentation generated from the ontology, a change process, versioning and an owner for each term.
- 5
Adoption
Mapping existing databases and APIs to the ontology, so it becomes the real shared model and not a PDF.
Tools and standards
- OWL 2, RDFS, SKOS
- SHACL
- Protégé, TopBraid, custom tooling
- Widoco and pyLODE documentation
- Git based ontology versioning and CI
What you get
- Ontology files with definitions and examples
- SHACL shapes and test suite
- Generated documentation site
- Mapping to your existing data
- Governance process
Frequently asked questions
What is the difference between a taxonomy, an ontology and a knowledge graph?+
A taxonomy is a hierarchy of terms. An ontology adds relationships, constraints and definitions. A knowledge graph is the data itself, structured according to an ontology or schema. Most projects need all three in different amounts.
Should we reuse an existing ontology?+
Usually partly. Standards such as schema.org, FOAF, PROV, FIBO or domain specific ones save time and make your data interoperable. We extend them where your business differs.
Related services
Knowledge Graph Development
Design and build knowledge graphs that connect your data and make it queryable by people, applications and AI.
GraphRAG and AI Agents
LLM applications grounded in your knowledge graph, so answers are traceable and agents act on facts.
AI Product Development
Full stack teams that ship AI powered web and mobile products: React, React Native, Node, voice and real time.
Talk to us about ontology engineering
Tell us about your data, your product or your AI use case. We reply within one business day with honest first thoughts.
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