Open Ontologies

AI-native ontology engineering. Generate OWL/RDF directly, validate with MCP tools, iterate until clean, govern with a Terraform-style lifecycle.

Prerequisites

This skill requires the Open Ontologies MCP server to provide the onto_* tools.

Install: cargo install open-ontologies or download from GitHub releases

MCP config (add to .mcp.json or Claude settings):

{
  "mcpServers": {
    "open-ontologies": {
      "command": "open-ontologies",
      "args": ["serve"]
    }
  }
}

No credentials needed. All processing runs locally in an in-memory Oxigraph triple store. Network access is only used when you explicitly call onto_pull (fetch remote ontology) or onto_push (send to SPARQL endpoint) with a URL you provide. Monitor alerts (onto_monitor) are logged to stdout only.

Core Workflow

When building or modifying ontologies, follow this workflow. Decide which tools to call and in what order based on results -- this is not a fixed pipeline.

1. Generate

• Understand the domain requirements (natural language, competency questions, methodology constraints)
• Generate Turtle/OWL directly -- Claude knows OWL, RDF, BORO, 4D modeling natively
• For complex methodologies, ask for background documents or constraints

2. Validate and Load

• Call onto_validate on the generated Turtle -- if it fails, fix syntax errors and re-validate
• Call onto_load to load into the Oxigraph triple store
• Call onto_stats to verify class count, property count, triple count match expectations

3. Verify

• Call onto_lint to check for missing labels, comments, domains, ranges -- fix any issues found
• Call onto_query with SPARQL to verify structure (expected classes, subclass hierarchies, competency questions)
• If a reference ontology exists, call onto_diff to compare

4. Iterate

• If any step reveals problems, fix the Turtle and restart from step 2
• Continue until validation passes, stats match, lint is clean, and SPARQL queries return expected results

5. Persist

• Call onto_save to write the final ontology to a .ttl file
• Call onto_version to save a named snapshot for rollback

Ontology Lifecycle (Terraform-style)

For evolving ontologies in production:

1. Plan -- onto_plan shows added/removed classes, blast radius, risk score. Check onto_lock for protected IRIs.
2. Enforce -- onto_enforce with a rule pack (generic, boro, value_partition) checks design pattern compliance.
3. Apply -- onto_apply with mode safe (clear + reload) or migrate (add owl:equivalentClass bridges).
4. Monitor -- onto_monitor runs SPARQL watchers with threshold alerts. Use onto_monitor_clear if blocked.
5. Drift -- onto_drift compares versions with rename detection and self-calibrating confidence.

Data Extension Workflow

When applying an ontology to external data:

1. onto_map -- generate mapping config from data schema + loaded ontology
2. onto_ingest -- parse structured data (CSV, JSON, NDJSON, XML, YAML, XLSX, Parquet) into RDF
3. onto_shacl -- validate against SHACL shapes (cardinality, datatypes, classes)
4. onto_reason -- run RDFS or OWL-RL inference, materializing inferred triples
5. Or use onto_extend to run the full pipeline: ingest, SHACL validate, reason in one call

Clinical Terminology Support

For healthcare ontologies:

• onto_crosswalk -- look up mappings between ICD-10, SNOMED CT, and MeSH
• onto_enrich -- add skos:exactMatch triples linking classes to clinical codes
• onto_validate_clinical -- check class labels against clinical crosswalk terminology

Ontology Alignment

For aligning two ontologies:

• onto_align -- detect alignment candidates (equivalentClass, exactMatch, subClassOf) using 6 weighted signals
• onto_align_feedback -- accept/reject candidates to self-calibrate confidence weights

Tool Reference

Usage Examples

Build a pizza ontology from scratch

Build me a pizza ontology with classes for Pizza, PizzaBase (ThinAndCrispy, DeepPan),
PizzaTopping (Mozzarella, Tomato, Pepperoni, Mushroom), and properties hasBase, hasTopping.
Include rdfs:labels and rdfs:comments on everything. Validate and run competency queries
to check I can ask "what toppings does a Margherita have?"

Load and query an existing ontology

Load the ontology from https://www.w3.org/TR/owl-guide/wine.rdf, show me stats,
lint it, and run a SPARQL query to find all subclasses of Wine.

Evolve an ontology safely

I need to add a new class "GlutenFreePizza" as a subclass of Pizza with a restriction
that hasBase only GlutenFreeBase. Plan the change, enforce against generic rules,
and apply in safe mode.

Ingest CSV data into a knowledge graph

I have a CSV of employees with columns: name, department, role, start_date.
Map it to the loaded HR ontology and ingest it. Then validate with SHACL shapes
and run inference to materialize department hierarchies.

Align two ontologies

Load schema.org and my company ontology. Run onto_align to find equivalentClass
and exactMatch candidates. I'll review and give feedback to calibrate the weights.

Key Principle

Dynamically decide the next tool call based on what the previous tool returned. If onto_validate fails, fix and retry. If onto_stats shows wrong counts, regenerate. If onto_lint finds missing labels, add them. The MCP tools are individual operations -- Claude is the orchestrator.