Tables with an RDF dress
There are two ways enterprises build an ontology and Stéphane Fellah thinks both of them are wrong. Top-down from a philosophical upper ontology buries the project in arguments about what a quality is. Bottom-up from your existing schemas re-imports every application-centric assumption you were trying to escape, and he has seen where that lands: 15,000 classes and properties that are, in his words, just tables with an RDF dress. His alternative starts somewhere less obvious, with the decision questions the business actually needs answered, which scope the model and double as unit tests. He closes on why agentic AI will not work until agents can describe themselves semantically and present credentials before they act.
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Stéphane Fellah
Geospatial semantics pioneer who entered the field through OGC standards in the late 1990s and now builds knowledge-graph-grounded AI, including his census.market work on verifiable agent assertions.
What we'll cover
- 01The two ways enterprises build ontologies that both fail: philosophical top-down (BFO, CCO) and bottom-up from existing schemas, and why the bottom-up version imports every assumption you were trying to escape.
- 02Competency questions as the unit of scope: how a decision question bounds a module, and how a SPARQL query turns it into an actual unit test.
- 03The three-layer build, cross-domain foundations (W3C ORG, SSN, DCAT), a business layer, then the vertical, and why small ontologies with few classes are the ones that survive.
- 04What an LLM-generated ontology looks like when nobody checks it: 15,000 classes and properties, a relational model in RDF clothing, zero interoperability.
- 05Why 80% of an ontologist's work is now automated and the remaining 20% is the whole job, plus why he stopped using Protégé and TopBraid.
- 06Implicit semantics as the actual root cause of interoperability failure: a lat/long column with no projection or unit, and the Mars orbiter that crashed over kilometres versus miles.
- 07Don't migrate your data, lift it. Adapters that turn API JSON into a graph, and why MCP returning JSON still leaves you with the same problem.
- 08A concrete learning path for web developers: JSON-LD, then RDF, RDFS, OWL, SHACL, SKOS, and where to look at open linked data in the wild.
- 09The closing argument: agentic AI needs a trust layer where agents present verifiable credentials and satisfy a contract before they act.
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