Open Ontologies

AI-native ontology engineering using 39+ MCP tools backed by an in-memory Oxigraph triple store. Build, validate, query, and govern RDF/OWL ontologies with a generate-validate-iterate loop. Use when building ontologies, knowledge graphs, RDF data, SPARQL queries, BORO/4D modeling, SHACL validation, clinical terminology mapping, or Terraform-style ontology lifecycle management.

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

Tool	When to use
onto_validate	After generating or modifying Turtle -- always validate first
onto_load	After validation passes -- loads into triple store
onto_stats	After loading -- sanity check on counts
onto_lint	After loading -- catches missing labels, domains, ranges
onto_query	Verify structure, answer competency questions
onto_diff	Compare against a reference or previous version
onto_save	Persist ontology to a file
onto_convert	Convert between formats (Turtle, N-Triples, RDF/XML, N-Quads, TriG)
onto_clear	Reset the store before loading a different ontology
onto_pull	Fetch ontology from a remote URL or SPARQL endpoint
onto_push	Push ontology to a SPARQL endpoint
onto_import	Resolve and load owl:imports chains
onto_version	Save a named snapshot before making changes
onto_history	List saved version snapshots
onto_rollback	Restore a previous version
onto_ingest	Parse structured data into RDF and load into store
onto_map	Generate mapping config from data schema + ontology
onto_shacl	Validate data against SHACL shapes
onto_reason	Run RDFS or OWL-RL inference
onto_extend	Full pipeline: ingest, SHACL validate, reason
onto_plan	Show added/removed classes, blast radius, risk score
onto_apply	Apply changes in safe or migrate mode
onto_lock	Protect production IRIs from removal
onto_drift	Compare versions with rename detection
onto_enforce	Design pattern checks: generic, boro, value_partition, or custom
onto_monitor	Run SPARQL watchers with threshold alerts
onto_monitor_clear	Clear blocked state after resolving alerts
onto_crosswalk	Look up clinical terminology mappings (ICD-10, SNOMED, MeSH)
onto_enrich	Add skos:exactMatch triples linking to clinical codes
onto_validate_clinical	Check class labels against clinical terminology
onto_align	Detect alignment candidates between two ontologies
onto_align_feedback	Accept/reject alignment candidates for self-calibrating weights
onto_lineage	View session lineage trail (plan, enforce, apply, monitor, drift)
onto_lint_feedback	Accept/dismiss lint issues to teach suppression
onto_enforce_feedback	Accept/dismiss enforce violations to teach suppression

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.