Ontology engineering

What an ontology is, and what it is for

An ontology is a formal, machine-readable model of a domain: what kinds of thing exist, what properties they have, what relationships can hold between them, and what constraints must hold. It is normally written in OWL 2, with SKOS carrying the controlled vocabulary and SHACL carrying the constraints. Writing it formally buys you one thing that a data dictionary or a Confluence page cannot: software can read it and reject a statement that contradicts it.

That capability is why ontologies came back. A retrieval pipeline can hand a language model relevant text, and the model will produce something fluent. Nothing in that loop can tell you the entity it just asserted does not exist. An ontology plus a closed-world check can, at the term level, cheaply, and without a human in the loop. We published the measurement behind that claim: open-world SHACL accepted all 300 data graphs we seeded with fabricated terms, and a closed-world vocabulary gate caught all 300 with no false positives on clean data.

The distinction people most often get wrong is between the three layers. A taxonomy is a hierarchy of concepts. An ontology adds the relationships and the constraints. A knowledge graph is the instance data underneath: the actual entities and the actual claims about them. The ontology says what can be said. The graph says what is being claimed. Our glossary covers the rest of the vocabulary, and the free 15-lesson ontology engineering course teaches the modelling itself, from RDF through SPARQL, SHACL and GraphRAG.

Questions we get asked

What is an ontology?

An ontology is a formal, machine-readable model of a domain: the kinds of thing that exist in it, the properties those things have, the relationships that can hold between them, and the constraints that must hold. In practice it is written in OWL 2, usually with SKOS for the controlled vocabulary and SHACL for the constraints. The point of writing it formally is that software can then reason over it and reject statements that contradict it, which prose documentation cannot do.

What is the difference between an ontology, a taxonomy and a knowledge graph?

A taxonomy arranges concepts in a hierarchy: broader and narrower, and little else. An ontology adds the relationships and the constraints, so it can express that a bank holding company controls a subsidiary and that control has to be dated and sourced. A knowledge graph is the instance layer: the actual banks, the actual subsidiaries, the actual dates. The ontology says what can be said; the knowledge graph says what is claimed. You can build a graph without an ontology, but then nothing can tell you the graph is wrong.

What is the difference between OWL and SHACL, and do I need both?

OWL 2 describes what is true in the domain and lets a reasoner infer more of it. SHACL checks whether a particular data graph conforms to a set of shapes. They answer different questions, so most real projects use both. The trap is assuming SHACL is a sufficient check. SHACL is open-world: if your data uses a property the shapes say nothing about, SHACL passes it. We measured this across three vocabularies and open-world SHACL accepted every one of 300 graphs carrying a fabricated term. If a language model is writing your RDF, you need a closed-world vocabulary gate as well.

Is the semantic web the same thing as a knowledge graph?

No, though the technologies overlap almost completely. The semantic web is the W3C programme and its stack: RDF for the data model, RDFS and OWL for the schema, SKOS for vocabularies, SHACL for validation, SPARQL for querying, and dereferenceable URIs so that data published by different people can be joined. Linked data is the publishing discipline that goes with it. A knowledge graph is the artefact you get: a graph of entities and relationships, usually stored in a triple store, that a system actually queries. Most enterprise knowledge graphs are built on the semantic web stack precisely because they need identifiers that survive being joined against somebody else’s data.

What is GraphRAG, and does it need an ontology?

GraphRAG retrieves over a knowledge graph rather than over a flat vector index, so the model gets structured neighbours and relationship paths instead of loose passages. It works better than plain retrieval on multi-hop questions. It does not need an ontology to run, and this is where projects go wrong: without a schema and a term-level check, the graph the pipeline builds inherits every entity the extraction step invented, and GraphRAG then retrieves that error confidently. The ontology is what lets you reject the bad node before it enters the graph.

Do AI agents and RAG pipelines actually need an ontology?

They need something that can tell them they are wrong, and an ontology is the cheapest thing that does that job at the term level. Retrieval gives a model relevant text; it does not give it a way to detect that the entity it just asserted does not exist. When we fine-tuned an open model on the UK IES4 standard, confabulated ontology terms fell from 93.7 per cent of outputs to 1.0 per cent, but the number that made that measurable at all was the closed-world check against the published vocabulary. Without it, fluent and wrong looks the same as fluent and right.

How long does an ontology project take, and what does it cost?

A scoped audit of an ontology or knowledge graph you already have takes about two weeks and sits below the £10,000 direct award threshold for public bodies. A domain ontology with a validated instance graph over real source data is typically six to twelve weeks depending on how many source registers have to be reconciled. Public sector buyers can commission through CCS RM6200 for build work or RM6126 for research and audit work.

Who builds ontologies in the UK?

The established UK and European names include Semantic Partners, Ontotext, DNV and the Ontology Engineering Group at Universidad Politécnica de Madrid, alongside in-house teams at large publishers and banks. Tesseract Academy works in the same space with a specific emphasis: everything we publish is reproducible from public data, every headline is computed at least two independent ways, and we report the hypotheses that died. Our work for the National Digital Twin Programme is open source under Apache 2.0.