Semantic modelling and semantic layer skills quiz for reliable AI

Semantic Modelling & the Semantic Layer:
Which Skills Do You Need for Reliable AI?

Find Your Semantic Modelling & Ontology Engineering Learning Path

Semantic modelling, knowledge graphs and ontology engineering cover a lot of ground — from building a shared business vocabulary to engineering ontologies that let AI act reliably. If you’re not sure where you fit, or which skills to build first, this free quiz points you to the right starting point in under five minutes.

Take the free quiz →

What the quiz tells you

The quiz maps your current skills and goals onto three routes through our Semantic Modelling & Ontology Engineering pathway:

  • Start with Course 1 — Building Shared Meaning. You work with data and models (ER, UML, BI) and want shared, machine-usable meaning. Ideal for BI and analytics engineers, data modellers, and teams building a semantic layer, data products or a mesh.

  • Go straight to Course 2 — Engineering Reliable Agentic Automation. You already model in RDF/OWL and code, and need to verify meaning formally, integrate across systems and APIs, and deploy AI that acts reliably.

  • Do both — the career track. You’re building an end-to-end capability, from shared meaning to reliable agentic automation, on one shared method and vocabulary.

Answer a few questions and you’ll get a recommended path, plus what to learn first.

Find your path →

What is semantic modelling?

Semantic modelling is the practice of capturing what your data means in a form both people and machines can use reliably — moving beyond familiar data models (ER, UML, relational) to property graphs, RDF and formal ontologies. It’s the foundation of the “semantic layer” every organisation is now trying to build. The catch is that “semantic layer” means at least five different things — a shared business vocabulary, a canonical model, a BI metrics layer, an integration layer across systems, and formal machine-readable meaning — and when those are built as disconnected point solutions, they drift apart. A sound semantic model builds them on one foundation.

Why it matters for reliable AI

It’s the problem AI teams keep rediscovering: agents fail on data that has no formal semantics. The shared meaning you build today is exactly what makes tomorrow’s agentic automation reliable. That’s why this pathway connects the two halves most organisations keep apart — the analysts and modellers who understand what the data means, and the engineers who build the systems — on one common method, taught entirely on open standards (RDF, OWL, SHACL, SKOS, Cypher/GQL), not locked to any single vendor.

FAQs

  • Semantic modelling builds shared, machine-usable meaning starting from the data models you already know — property graphs, RDF, a shared vocabulary and an analytics semantic layer (Course 1). Ontology engineering goes further: authoring and formally verifying ontologies, standing up an operational semantic layer, and grounding AI so it acts reliably (Course 2).

  • Not for Course 1 — it assumes only everyday comfort with data and models (ER, UML, relational), with no logic or programming required. Course 2 is more advanced and does assume programming familiarity (e.g. Python) alongside the RDF/OWL basics from Course 1.

  • Most people start with Course 1, which builds the shared-meaning foundation. You can go straight to Course 2 if you already work in RDF/OWL and code. The quiz on this page will point you to the right starting point for your role and experience.

  • From Course 1: model domains as property graphs and RDF, query with Cypher and SPARQL, and build a shared vocabulary and analytics semantic layer. From Course 2: author and formally verify ontologies, engineer knowledge graphs and pipelines, and build grounded, reliable agentic automation.

  • BI and analytics engineers, data modellers, semantic-layer and mesh teams, analysts, and information, solution and AI engineers — anyone who needs data to mean something machines can act on reliably.

  • About five minutes. It’s free, with no exam and no commitment — just a recommended path and next step.

Take the quiz →