Knowledge Graph Instantiation
Turn your data into a connected, queryable knowledge graph.
Takes source records and an ontology schema and builds the graph: typed entity nodes, typed edges, provenance. The output is a live, traversable graph your systems can query — not a flat table or a document store. Reach for this when relationships between entities matter as much as the entities themselves.
Shape
Operational dimensions
Runs without a person in the path.
Fires when an upstream condition occurs.
Owns a system-of-record; expensive to migrate.
Reads from and writes to external systems.
Inputs
- source records (structured or semi-structured)
- schema / ontology definition
- field-to-entity mapping rules
- canonical identifier decisions from entity resolution
Outputs
- populated typed graph (nodes + edges + properties + provenance)
- graph query interface (traversal, neighbourhood, path)
- ingestion exception log
Mechanism
Instantiates entity and relationship records against a defined schema/ontology, producing a queryable graph of typed nodes and typed edges.
Why this is a primitive
Cannot be decomposed: the act of materialising typed entities + typed relationships into a graph store is a single operation. It assumes a schema exists (that's vocabulary-authoring), assumes the entities have been deduped (that's entity-resolution), and assumes inputs are conformant (that's schema-normalisation) — it does the graph-building step only. Strip the instantiation and you have an ontology with nothing instantiated against it.
Where it shows up
Related primitives
Tags
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Primitives are configured into named solution shapes for each client’s domain. The fastest next step is a conversation about which shape fits your problem.
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