Industrial knowledge graphs
◐ In active development — with the ontology layer ● running today · updated
An industrial knowledge graph is the populated, connected instance of an operation’s ontology: every real asset, procedure, person, product, and event as nodes, with their relationships as edges — a living map that systems can traverse and reason over, not just search.
DeemL is a know-how activation platform: it captures expert knowledge and activates it as guided, governed work.
What it is
Ontology and graph are schema and instance: the ontology says “assets have failure modes; procedures apply to asset classes”; the graph says “Slitter Line-2 had this failure in March, solved by this procedure, authored by this expert, superseded by version 7.” The graph’s power is traversal — following edges to answer questions no document contains: which procedures are affected if this component changes? Where else does this failure pattern appear? Whose expertise covers this asset class, and what did they capture?
Why it matters for industrial knowledge work
Industrial questions are relationship questions. “What changed before the defect rate moved” and “what does the new hire need for this line” are graph traversals wearing prose. An operation whose knowledge is graph-structured can ask them of a system; one whose knowledge is document-structured asks them of whoever’s been there longest.
Where DeemL stands
The Target Zones ontology structures every capture against assets, locations, roles, and products; delivery and retrieval already traverse those mappings — it’s why guidance finds the worker and answers scope to context.
Deeper relationship types, cross-entity traversal, and reasoning over the populated graph (impact analysis, expertise mapping, pattern surfacing) are active development on the shipped ontology base.
FAQ
What’s the difference between a knowledge graph and a knowledge base?
A base stores content; a graph stores content and its relationships, machine-traversably — so systems can answer questions that span entities instead of retrieving single documents.
Do I need a data-science team to get a knowledge graph?
Not in this architecture: the graph accretes from normal use — every capture mapped to its contexts adds nodes and edges — rather than from a modeling project.