Criticality analysis for CoyPu Knowledge Graph ontologies. Identifies structurally critical concepts across six OWL/RDF ontologies and generates simulation shock parameters from live KG data.

Given a set of CoyPu ontology files (TTL), the analyser computes a criticality score for each concept along three dimensions:

Score formula: 0.4 × betweenness + 0.3 × concentration + 0.3 × cascade + 0.2 bonus (articulation points)

Levels: CRITICAL (≥ 0.7) | HIGH (0.5–0.7) | MEDIUM (0.3–0.5) | LOW (< 0.3)

The tool works with the six CoyPu sub-ontologies:

• coypu-global — core entities (Event, Feature, Country, …)
• gta — Global Trade Alert interventions
• ta — Trade agreements
• vtf — Value chain / supply network
• world-port-index — Port infrastructure (WPI)
• em-dat — Natural disaster data

pip install -e ".[dev]"

Requirements: Python 3.9+, ontology TTL files from the CoyPu project.

coypu-kg-analyser analyse \
  --ontology-dir /path/to/ontology/ \
  --output-dir ./output/ \
  --format all \
  --min-criticality medium

Output formats (--format): json, yaml, markdown, sparql, all

Match criticality results against a PDL scenario file:

coypu-kg-analyser enrich-scenario \
  --scenario s1-soja.pdl.yaml \
  --results output/results.json \
  --output s1-soja-enriched.pdl.yaml

Query the CoyPu SPARQL endpoint directly:

coypu-kg-analyser query-live \
  --endpoint https://copper.coypu.org/coypu/ \
  --concept coy:Event

Translate live KG data (ACLED conflict events, GTA trade interventions, crop production statistics) into simulation shock parameters for the soybean supply chain scenario:

coypu-kg-analyser parametrize-s1 \
  --output shocks.json \
  --lookback-days 180

The output shocks.json contains ready-to-use shock parameters for agent-based supply chain simulation.

# Run tests
pytest tests/ -v

# Run with coverage
pytest --cov=coypu_kg_analyser tests/

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