Build a working ontology from defence logistics data

Chapter 2 created a small ontology progressively, ending with 25 triples and a relationship query. Now use the complete importer to add stock snapshots, allocations, inspection evidence, and source lineage. The baseline produces 121 instance triples. The smaller lesson and this complete build use separate output directories, so you can compare them.

Keep the original fixture unchanged for this first run. An expected result is useful only if the inputs are known. The reference clock is fixed at 14 September 2026, 08:00 UTC, so the same dated records can produce reproducible freshness checks.

From the lab directory, run the build with the environment created in chapter 2:

The command reads the CSV fixtures and constructs schema/schema.ttl, schema/shapes.ttl, results/instances.ttl, and results/dataset.trig, alongside provenance and build records. Turtle represents the populated statements; TriG carries the dataset's separate named graphs. These files are produced from the functions in ontology_lab.py; they are not the persistent place to edit the model.

Start by following the work-order row. Its fields have specific destinations:

The mapper resolves those aliases through the explicit table before writing the relationships. It must not create a new generator simply because a source uses a different nickname. An unresolved reference is an ingestion problem to fix, not an invitation to invent an entity.

These selected triples show the resulting work order and its typed targets. They are a readable excerpt of the instance graph, with prefixes added for clarity:

Save this complete script as inspect_order.py in the lab directory. It reads the generated RDF and prints the work order's linked records. Using the class and identifier fields makes the example independent of Turtle's formatting or statement order.

Run it using the same environment:

The supplied fixture produced these fields when the script was checked:

You have followed a schema-to-instance relationship using the actual graph. The script inspects just the work order; the full query also checks stock, allocations, and evidence before calculating availability.

Execute the validator explicitly:

The validator applies the supplied shapes and writes results/validation-report.txt and .ttl. For the baseline, its console result is:

A passing report establishes that the data satisfies those selected conditions. It does not certify physical stock, legal authority, or overall maintenance readiness. SHACL evaluates the conditions provided to it; the coverage of those conditions remains an engineering decision.³⁰¹

Read a failure at the level of the record and field. A missing asset relationship needs an identity or mapping correction. An invalid quantity needs the unit and source value checked. Rebuilding from corrected source records preserves the explanation of what changed; editing the generated output merely to remove an error can hide the underlying defect.

The program also checks business invariants beyond simple datatypes. North's six recorded filter units include two quarantined units and three separately reserved units. Those allocation sets must be disjoint and refer to the intended snapshot. Correct integers attached to the wrong snapshot would still produce an unreliable answer.

Run the scoped query:

The supplied fixture produces one available filter and a shortage of one. The result retains the inputs: required two, on hand six, quarantined two, reserved three, and unit each. A bare answer of “no” would prevent the coordinator from seeing whether the problem was stock, stale evidence, or an incorrect mapping.

The command writes results/explanation.json and results/human-review-request.json. The request is marked UNSUBMITTED_LOCAL_DRAFT and changes no inventory. The organization argument selects a local test scope; Chapter 5 collects the implemented and production-required boundaries in one table.

Chapter 4 opens the query itself and examines the calculation. Before moving on, follow results/provenance.ttl back to the source row and file. The build uses PROV entities and activities to record derivation.³⁰² Locators such as orders.csv#record=2 identify CSV records, counting the header as record 1. Hashes in results/manifest.json identify input bytes; they do not prove the supplier was correct. A later source revision requires a new, checked build.

Unstructured maintenance notes would need an additional intake path. A future extractor could read “the two North filters are on hold” and propose an allocation candidate. That phrase alone does not establish which snapshot or individual units it concerns, or whether “on hold” means reservation or quarantine. Preserve the passage and ask for the missing context.

The proposed extension is: source passage → candidate entities and relationships → canonical identity resolution → explicit checks → human review → published facts. It is not implemented in this lab. The current runnable path ingests the structured CSV fixture deterministically. The PDF extraction guide explains the separate document-reading problem.

Fasina, Sawyerr, and Murainah report precision 0.822, recall 0.824, and F1 0.823 for their WebNLG text-to-RDF experiment.³⁰³ Those are the authors' measurements, not results from this maintenance lab. Their approach motivates checking extracted candidates; a high extraction score still does not establish physical stock or operational authority.

An extractor's confidence is a review signal, not a truth label. OWL's open-world semantics also mean missing knowledge is not automatically false; OWL is not a validation firewall.³⁰⁴ Keep uncertainty, contradictions, and unresolved references outside accepted operational facts until the relevant checks and reviewers resolve them.

For a future extraction test, label the expected facts first. If a synthetic trial has 9 correct predictions, 3 incorrect predictions, and 1 missed fact, its illustrative scores are:

That arithmetic is a teaching example, not an executed neural evaluation. Record which kinds of mistakes matter: confusing quarantine with reservation may be more consequential than missing a descriptive nickname.

You have the complete schema, shapes, populated RDF, source lineage, validation report, and one-filter shortage result. Confirm that the inspection script prints GEN-17, PF-9, two, and each. Find orders.csv#record=2 in the provenance and compare its source values with the graph. Chapter 4 provides an interactive viewer of these actual exported records and explains the query and integration contract.