Industrial ontologies
● Running in the platform today · updated
An industrial ontology is a structured model of an operation’s real-world entities — assets, lines, products, roles, procedures — and the relationships between them, so that information systems can attach knowledge to the exact context where it applies rather than storing it as unstructured documents.
DeemL is a know-how activation platform: it captures expert knowledge and activates it as guided, governed work.
What it is
An ontology answers the question every knowledge system eventually fails on: where does this knowledge belong? A troubleshooting fix isn’t general — it belongs to a machine, a failure mode, a product variant. An ontology makes those relationships explicit and machine-usable: this procedure applies to this asset class, is performed by this role, matters for this product family. Without one, knowledge retrieval is text search and luck; with one, it’s placement.
Why it matters for industrial knowledge work
Industrial knowledge is the most context-bound knowledge there is — the fix for line 3 is not the fix for line 4. The ontology is what lets guidance find the worker instead of the worker hunting for guidance: the technician standing at a machine sees that machine’s history, procedures, and judgment, because the system knows what “that machine” means.
Where DeemL stands
DeemL ships a working industrial ontology: the Target Zones model maps every piece of captured knowledge to the operational contexts where it applies — assets, locations, roles, products — and delivery is scoped by it. It’s why a work order can arrive with its know-how attached, and why Ask DD answers from where you’re standing, not from the whole library.
FAQ
What’s a concrete example of an industrial ontology?
A model that knows “Slitter — Line 2” is an instance of an asset class, located in a plant, run by certain roles, subject to certain procedures — so a capture about that slitter automatically reaches the people and moments it applies to.
How is an ontology different from tags or folders?
Tags are flat labels; an ontology encodes relationships (this procedure applies to this asset class, performed by this role) that systems can reason over.
Do I have to build the ontology myself?
No — it grows from your operation’s own structure as knowledge is captured and mapped; it’s an operating layer, not a modeling project.