{
  "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 | | 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 | | 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 | | onto_sql_ingest | Run SQL SELECT against PostgreSQL or DuckDB → RDF (DuckDB enables federation over CSV/Parquet/JSON/HTTPFS/postgres-scanner via its extensions) |

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.

From source (Rust 1.85+):

git clone https://github.com/fabio-rovai/open-ontologies.git
cd open-ontologies && cargo build --release
./target/release/open-ontologies init

Connect to your MCP client

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "open-ontologies": {
      "command": "/path/to/open-ontologies/target/release/open-ontologies",
      "args": ["serve"]
    }
  }
}

Restart Claude Code. The onto_* tools are now available.

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "open-ontologies": {
      "command": "/path/to/open-ontologies/target/release/open-ontologies",
      "args": ["serve"]
    }
  }
}

Add to .cursor/mcp.json or equivalent:

{
  "mcpServers": {
    "open-ontologies": {
      "command": "/path/to/open-ontologies/target/release/open-ontologies",
      "args": ["serve"]
    }
  }
}

{
  "mcpServers": {
    "open-ontologies": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "ghcr.io/fabio-rovai/open-ontologies", "serve"]
    }
  }
}

Build your first ontology

Build me a Pizza ontology following the Manchester University tutorial.
Include all 49 toppings, 24 named pizzas, spiciness value partition,
and defined classes (VegetarianPizza, MeatyPizza, SpicyPizza).
Validate it, load it, and show me the stats.

Claude generates Turtle, then runs the full pipeline automatically:

onto_validate → onto_load → onto_stats → onto_reason → onto_stats → onto_lint → onto_enforce → onto_query → onto_save → onto_version

Every build includes OWL reasoning (materializes inferred triples), design pattern enforcement, and automatic versioning.

Studio (Desktop App)

The Studio is a native desktop application that wraps the same engine in a visual environment — no browser, no server to manage. It runs entirely on your machine: the engine sidecar handles RDF/OWL operations while the UI renders the graph in real time.

Think of it as Protege meets an AI copilot. Type "build ontology about cats" and watch a 1,400-class ontology appear in the tree — classes, properties, individuals, and axioms built automatically across 13 pipeline steps. Click any node to inspect its triples, trace connections via clickable pills, and follow every change through the lineage panel.

Why virtualized tree (not 3D graph)

Prior to v0.1.12, the Studio used a D3.js horizontal tree and a 3D force-directed graph (Three.js / WebGL). Both worked for small ontologies (~100 classes) but became unusable at IES-level depth: the D3 tree couldn't handle 500+ nodes without layout thrashing, and the 3D graph froze the WebKit webview above 1,000 nodes.

The v2 deep builder changed the equation — a single /build command now produces 1,400+ classes. We replaced both views with a virtualized DOM tree: only visible rows exist in the DOM (constant memory regardless of ontology size), with hierarchy connector lines, type-filtered legend, search, breadcrumb navigation, and a connections panel. This handles the full IES Common (511 classes) and deep-built ontologies (1,400+ classes) without lag.

How it works

The Studio launches three processes that communicate locally:

1. Tauri 2 shell — native window (macOS/Linux/Windows) with a WebKit webview
2. Engine sidecar — the same Rust binary, running as an HTTP MCP server on localhost:8080
3. Agent sidecar — Node.js process running Claude via the Agent SDK, connected to the engine over MCP

When you type in the chat panel, your message goes to the Agent sidecar, which sends it to Claude. Claude decides which onto_* tools to call, the engine executes them, and the UI refreshes the graph. The entire loop — prompt to visual update — takes seconds.

Install and run

Prerequisites: Rust + Cargo · Node.js 18+

# 1. Build the engine binary (from repo root)
cargo build --release

# 2. Install JS dependencies
cd studio && npm install

# 3. Run
PATH=/opt/homebrew/bin:~/.cargo/bin:$PATH npm run tauri dev

The first launch compiles the Tauri shell (~2 min). Subsequent launches start in seconds.

Features

| Feature | Description | | --- | --- | | Virtualized Tree | Ontology explorer that handles 1,500+ classes without lag. Hierarchy connector lines, collapsible branches, type-filtered legend (Class/Property/Individual), search with auto-expand, breadcrumb path navigation, and a connections panel showing domain/range relationships as clickable pills. Only visible rows are in the DOM — constant memory regardless of ontology size. | | AI Agent Chat | Natural language ontology engineering via Claude Opus 4.6 + Agent SDK. Two build modes: /build runs a 13-step pipeline producing IES-level ontologies (500-1,500+ classes, 100-200+ properties), /sketch runs 3 steps for quick prototyping (~80 classes). Each tool call is shown in real time. | | Property Inspector | Protege-style inline triple editor. Click any node to see its rdfs:subClassOf, rdfs:label, rdfs:domain, rdfs:range and all other triples. Edit in place, hover to delete, + Add for new triples. Changes are immediately reflected in the graph. | | Lineage Panel | Full audit trail from SQLite: every plan, apply, enforce, drift, monitor, and align event, grouped by session with timestamps. See exactly what Claude did and in what order. | | Named Save | ⌘S to save as ~/.open-ontologies/<name>.ttl. Auto-saves to studio-live.ttl after every mutation so you never lose work. |

Keyboard shortcuts

| Shortcut | Action | | --- | --- | | ⌘J | Toggle AI chat panel | | ⌘I | Toggle property inspector | | ⌘S | Save ontology | | F | Fit graph to viewport (tree view) | | R | Reset zoom (tree view) | | Esc | Deselect node | | Shift+click | Collapse/expand branch (tree view) | | Scroll | Zoom in/out | | Click + drag | Pan |

Benchmarks

OntoAxiom — LLM Axiom Identification

OntoAxiom tests axiom identification across 9 ontologies and 3,042 ground truth axioms.

| Approach | F1 | vs o1 (paper best) | | --- | --- | --- | | o1 (paper's best) | 0.197 | — | | Bare Claude Opus | 0.431 | +119% | | MCP extraction | 0.717 | +264% |

Pizz