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 --git https://github.com/fabio-rovai/open-ontologies --features embeddings,plugins,sql, or download from GitHub releases, which ships the default feature set without embeddings, SQL or plugins. The crate is not on crates.io yet, so plain cargo install open-ontologies does not work
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:
- Plan --
onto_plan shows added/removed classes, blast radius, risk score. Check onto_lock for protected IRIs.
- Enforce --
onto_enforce with a rule pack (generic, boro, value_partition) checks design pattern compliance.
- Apply --
onto_apply with mode safe (clear + reload) or migrate (add owl:equivalentClass bridges).
- Monitor --
onto_monitor runs SPARQL watchers with threshold alerts. Use onto_monitor_clear if blocked.
- Drift --
onto_drift compares versions with rename detection and self-calibrating confidence.
Data Extension Workflow
When applying an ontology to external data:
onto_map -- generate mapping config from data schema + loaded ontologyonto_ingest -- parse structured data (CSV, JSON, NDJSON, XML, YAML, XLSX, Parquet) into RDFonto_shacl -- validate against SHACL shapes (cardinality, datatypes, classes)onto_reason -- run RDFS or OWL-RL inference, materializing inferred triples- 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 MeSHonto_enrich -- add skos:exactMatch triples linking classes to clinical codesonto_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 signalsonto_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 |
onto_unload | Unload from memory. With name targets a specific cached entry; delete_cache=true also removes the on-disk file |
onto_recompile | Re-parse the source. With name rebuilds a non-active cached entry without disturbing the active in-memory store |
onto_cache_status | Inspect compile cache: active slot, all entries, effective [cache] config |
onto_cache_list | List cached ontologies with metadata (is_active, in_memory, mtime, size) |
onto_cache_remove | Remove a cached ontology by name (pass delete_file=false to keep the on-disk N-Triples) |
onto_repo_list | List RDF/OWL files in configured [general] ontology_dirs directories |
onto_repo_load | Load an ontology from a configured repo by bare name, relative path, or absolute path |
onto_status | Server health / loaded triple count |
onto_marketplace | Browse / install standard ontologies from the curated catalogue |
onto_dl_check | Check subClass ⊑ superClass via DL tableaux |
onto_dl_explain | Explain why a class is unsatisfiable (DL clash trace) |
onto_embed | Generate text + Poincaré structural embeddings for all classes. Requires a build with --features embeddings; returns an error otherwise |
onto_search | Natural-language query → most-similar classes |
onto_similarity | Cosine + Poincaré distance between two IRIs |
onto_import_schema | Introspect PostgreSQL or DuckDB schema → generate OWL classes/properties/cardinality. Requires a build with --features postgres or --features duckdb |
onto_sql_ingest | Run SQL SELECT against PostgreSQL or DuckDB → RDF (DuckDB enables federation over CSV/Parquet/JSON/HTTPFS/postgres-scanner via its extensions). Requires a build with --features postgres or --features duckdb |
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.