Open Ontologies

A Terraforming MCP for Knowledge Graphs
Validate, classify, and govern AI-generated ontologies. Written in Rust. Ships as a single binary.

Quick Start · Studio · Benchmarks · IES · Tools · Architecture · Docs

Open Ontologies is a Rust MCP server and desktop Studio for AI-native ontology engineering. It exposes 43 tools that let Claude build, validate, query, diff, lint, version, reason over, align, and persist RDF/OWL ontologies using an in-memory Oxigraph triple store — with Terraform-style lifecycle management, a marketplace of 32 standard ontologies, clinical crosswalks, semantic embeddings, and a full lineage audit trail.

The Studio wraps the engine in a visual desktop environment: virtualized ontology tree with hierarchy lines, breadcrumb navigation, and connection explorer; AI chat panel with /build (IES-level deep) and /sketch (quick prototype) commands; Protégé-style property inspector; and lineage viewer.

No JVM. No Protégé.

Quick Start (MCP / CLI)

Install

Pre-built binaries:

# macOS (Apple Silicon)
curl -LO https://github.com/fabio-rovai/open-ontologies/releases/latest/download/open-ontologies-aarch64-apple-darwin
chmod +x open-ontologies-aarch64-apple-darwin && mv open-ontologies-aarch64-apple-darwin /usr/local/bin/open-ontologies

# macOS (Intel)
curl -LO https://github.com/fabio-rovai/open-ontologies/releases/latest/download/open-ontologies-x86_64-apple-darwin
chmod +x open-ontologies-x86_64-apple-darwin && mv open-ontologies-x86_64-apple-darwin /usr/local/bin/open-ontologies

# Linux (x86_64)
curl -LO https://github.com/fabio-rovai/open-ontologies/releases/latest/download/open-ontologies-x86_64-unknown-linux-gnu
chmod +x open-ontologies-x86_64-unknown-linux-gnu && mv open-ontologies-x86_64-unknown-linux-gnu /usr/local/bin/open-ontologies

Docker:

docker pull ghcr.io/fabio-rovai/open-ontologies:latest
docker run -i ghcr.io/fabio-rovai/open-ontologies serve

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

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

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

{
  "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

Keyboard shortcuts

Benchmarks

OntoAxiom — LLM Axiom Identification

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

Pizza Ontology — Manchester Tutorial

One sentence input: "Build a Pizza ontology following the Manchester tutorial specification."

/sketch vs /build — Two Build Modes

The Studio provides two build commands for different use cases. Both take the same input — "build ontology about cats" — but produce very different results:

/sketch runs 3 steps: classes + properties in one Turtle block, axioms + individuals, then save. Good for quick domain exploration or demo prototyping. Produces a complete ontology with hierarchy, properties, and individuals — but at a fraction of the depth.

/build runs a 13-step pipeline within a single persistent Claude session: foundation classes → per-branch deepening (4 passes) → gap filling → object properties (2 batches) → datatype properties → disjoints → individuals → reason → save. Each step focuses on one aspect of the ontology, staying within output token limits while building on the previous step's context. The result exceeds IES Common on every metric.

/sketch is comparable to the Pizza benchmark (95 classes, 8 properties). /build produces IES-level ontologies — deep enough for production use.

Mushroom Classification — OWL Reasoning vs Expert Labels

Dataset: UCI Mushroom Dataset — 8,124 specimens classified by mycology experts.

Ontology Marketplace — 32 Standard Ontologies

All 32 marketplace ontologies fetched, owl:imports resolved, loaded, and reasoned over with both RDFS and OWL-RL profiles:

32/32 ontologies loaded, imports resolved, and reasoned. RDFS adds 18% more triples. OWL-RL adds 41% — transitive/symmetric/inverse properties and equivalentClass expansion discover significantly more implicit knowledge. Schema.org jumps from +4,031 (RDFS) to +13,670 (OWL-RL) inferred triples in 117ms.

Reasoning Performance — vs HermiT

LUBM Scaling (load + reason cycle)

Full benchmark writeup: docs/benchmarks.md

OAEI Ontology Alignment — Anatomy Track

OAEI is the standard benchmark for ontology alignment systems. The Anatomy track aligns 2,737 mouse anatomy classes to 3,304 human anatomy classes against 1,516 reference mappings.

Open Ontologies uses 7 weighted signals (label similarity, property/parent/instance/restriction/neighbourhood overlap, embedding similarity), stable 1-to-1 matching, and a label penalty when no structural evidence is available. No external background knowledge (UMLS, BioPortal) is used. See issues #8, #9, #10 for planned improvements.

IES Support

IES (Information Exchange Standard) is the UK National Digital Twin Programme's core ontology framework. It uses a 4D extensionalist (BORO) approach for modelling entities, events, states, and relationships. Open Ontologies supports the full IES stack — all three layers, SHACL shapes, and example datasets from the IES-Org GitHub repositories.

The IES Layers

The marketplace includes all three tiers of the IES framework:

onto_marketplace install ies-top     # ToLO — BORO foundations (~22 classes)
onto_marketplace install ies-core    # Core — persons, states, events (~131 classes)
onto_marketplace install ies         # Common — full ontology (511 classes, 206 properties)

Benchmark

IES is the second-largest ontology in the marketplace by class count (after Schema.org). RDFS reasoning produces the richest inference gain of any non-general ontology — State, ClassOfEntity, and Event subclasses all generating deep transitive chains.

Example Data

Load IES example datasets directly from the official repositories:

onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/event-participation.ttl
onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/examples/sample-data/hospital.ttl
onto_pull https://raw.githubusercontent.com/telicent-oss/ies-examples/main/additional_examples/ship_movement.ttl

SHACL Validation

onto_pull https://raw.githubusercontent.com/IES-Org/ont-ies/main/docs/specification/ies-common.shacl
onto_shacl

Data Mapping: EPC → IES

The repo includes a sample of real UK Energy Performance Certificates (benchmark/epc/epc-sample.csv) with a mapping config that transforms tabular EPC data into IES-shaped RDF:

onto_load benchmark/generated/ies-building-extension.ttl
onto_ingest benchmark/epc/epc-sample.csv --mapping benchmark/epc/epc-ies-mapping.json
onto_reason --profile rdfs

This mirrors NDTP's actual pipeline: CSV → IES RDF → validate → reason → query.

IES Building Extension — Comparison with NDTP/IRIS

The repo includes an IES Building Extension built from the UK EPC data schema and building science fundamentals, using IES 4D patterns. It was built independently — without reference to any existing implementation — then compared against the NDTP/IRIS production building ontology used in government data pipelines.

Built blind from the 105-column EPC schema, SAP methodology, and BORO 4D extensionalism — zero reference to the IRIS implementation. The two ontologies make different trade-offs: IRIS is more tightly curated with higher average hierarchy depth (2.89 vs 2.02), reflecting deliberate grouping by domain experts. Open Ontologies covers more of the EPC data schema and applies the BORO 4D pattern more systematically across the domain.

How the hierarchy emerges from building science

The ontology's depth (max 10 levels) is not hand-tuned — it follows the natural classification that building scientists use. The EPC data schema describes heating systems as flat text fields ("Condensing gas boiler with radiators"), but the underlying domain has layered structure:

graph TD
    HS[Heating System] --> CH[Central Heating]
    HS --> NC[Non-Central / Room Heating]

    CH --> WET[Wet Central Heating<br/><i>hydronic distribution</i>]
    CH --> WA[Warm Air Central Heating<br/><i>ducted air</i>]
    CH --> EC[Electric Central Heating<br/><i>storage / underfloor</i>]

    WET --> BB[Boiler-Based]
    WET --> HP[Heat Pump]
    WET --> DH[Community / District]

    BB --> CB[Combustion Boiler]
    BB --> CHP[Micro-CHP]

    CB --> GAS["Gas boiler"]
    CB --> OIL["Oil boiler"]
    CB --> LPG["LPG boiler"]
    CB --> COND["Condensing boiler"]
    CB --> COMBI["Combi boiler"]
    CB --> BACK["Back boiler"]

    HP --> ASHP["Air source"]
    HP --> GSHP["Ground source"]
    HP --> WSHP["Water source"]

    EC --> STOR["Storage heaters"]
    EC --> PNL["Panel heaters"]
    EC --> UF["Underfloor electric"]

    NC --> FIX[Fixed Room Heater]
    NC --> PORT[Portable Heater]

    FIX --> GROOM["Gas room heater"]
    FIX --> EROOM["Electric room heater"]
    FIX --> SFROOM["Solid fuel room heater"]

    style HS fill:#1a1a2e,color:#fff
    style CH fill:#16213e,color:#fff
    style NC fill:#16213e,color:#fff
    style WET fill:#0f3460,color:#fff
    style WA fill:#0f3460,color:#fff
    style EC fill:#0f3460,color:#fff
    style BB fill:#533483,color:#fff
    style HP fill:#533483,color:#fff
    style DH fill:#533483,color:#fff
    style CB fill:#e94560,color:#fff
    style CHP fill:#e94560,color:#fff

The same pattern applies to the building fabric — heat transfer physics dictates the grouping:

graph TD
    TE[Building Thermal Envelope] --> OP[Opaque Elements<br/><i>conduction-dominated</i>]
    TE --> TR[Transparent Elements<br/><i>radiation + conduction</i>]

    OP --> WALL[Walls]
    OP --> ROOF[Roofs]
    OP --> FLOOR[Floors]

    TR --> WIN[Windows]
    TR --> DOOR[Doors]

    WALL --> MAS[Masonry Walls<br/><i>thermal mass</i>]
    WALL --> FRM[Framed Walls<br/><i>stud bridges</i>]

    MAS --> CAV["Cavity wall"]
    MAS --> SOL["Solid brick"]
    MAS --> SND["Sandstone"]
    MAS --> GRN["Granite"]
    MAS --> COB["Cob"]

    FRM --> TF["Timber frame"]
    FRM --> SYS["System-built"]
    FRM --> PH["Park home"]

    ROOF --> PIT[Pitched Roof]
    ROOF --> FLT[Flat Roof]

    PIT --> COLD["Cold roof<br/><i>insulation at ceiling</i>"]
    PIT --> WARM["Warm roof<br/><i>insulation at rafter</i>"]
    PIT --> THATCH["Thatched"]

    WIN --> SGL["Single glazed"]
    WIN --> DBL["Double glazed"]
    WIN --> TPL["Triple glazed"]
    WIN --> SEC["Secondary glazing"]

    style TE fill:#1a1a2e,color:#fff
    style OP fill:#16213e,color:#fff
    style TR fill:#16213e,color:#fff
    style WALL fill:#0f3460,color:#fff
    style ROOF fill:#0f3460,color:#fff
    style FLOOR fill:#0f3460,color:#fff
    style WIN fill:#0f3460,color:#fff
    style DOOR fill:#0f3460,color:#fff
    style MAS fill:#533483,color:#fff
    style FRM fill:#533483,color:#fff
    style PIT fill:#533483,color:#fff
    style FLT fill:#533483,color:#fff

Each level in the tree is a real building science distinction — central vs room heating, hydronic vs warm air, combustion vs electric, masonry vs framed, cavity vs solid. An independent building scientist, given the same EPC data values, produces these same intermediate groupings (verified by clean-room reproduction). RDFS reasoning traverses these chains transitively, which is why a 10-level hierarchy generates 662 inferred triples from 3,229 raw.

EPC Column Coverage Benchmark

Both ontologies tested against 36 key EPC data columns — can each ontology receive and represent the data from that column?

Queries derived from published DESNZ/ONS EPC statistical reports — not from either ontology's class structure. Full benchmark: benchmark/epc/

Use onto_align to map it to other domain ontologies:

onto_load benchmark/generated/ies-building-extension.ttl
onto_align <other-ontology.ttl>

Hierarchy Enforcement — Automated Inference Improvement

The hierarchy enforce pack detects flat spots in any ontology and suggests intermediate grouping classes. This is the same process used to deepen the building extension — now codified as a repeatable tool:

onto_load my-ontology.ttl
onto_enforce --pack hierarchy
# → flags classes with >5 direct children
# → reports max depth, avg depth, hierarchy density

Tested on IES Common (511 classes), the tool found 24 flat spots. A clean-room agent — with no prior context — proposed 38 intermediate grouping classes based solely on the domain meaning of the flagged children:

graph LR
    subgraph Before["IES Common — before"]
        EP1[EventParticipant] --> P1["Prosecutor"]
        EP1 --> P2["Observer"]
        EP1 --> P3["Driver"]
        EP1 --> P4["Supplier"]
        EP1 --> P5["WeaponLocation"]
        EP1 --> P6["...52 direct children"]
    end

    subgraph After["IES Common — after hierarchy enforce"]
        EP2[EventParticipant] --> R[RoleInEvent]
        EP2 --> L[LocationInEvent]
        EP2 --> A[AssetInEvent]
        R --> LR2[LegalRole]
        R --> IR[InvestigativeRole]
        R --> CR[CommercialRole]
        LR2 --> Q1["Prosecutor"]
        LR2 --> Q2["Signatory"]
        IR --> Q3["Observer"]
        IR --> Q4["Investigator"]
        CR --> Q5["Supplier"]
        CR --> Q6["Negotiator"]
        L --> Q7["WeaponLocation"]
        L --> Q8["TargetLocation"]
        A --> Q9["VehicleUsed"]
    end

    style EP1 fill:#e94560,color:#fff
    style EP2 fill:#1a1a2e,color:#fff
    style R fill:#16213e,color:#fff
    style L fill:#16213e,color:#fff
    style A fill:#16213e,color:#fff
    style LR2 fill:#0f3460,color:#fff
    style IR fill:#0f3460,color:#fff
    style CR fill:#0f3460,color:#fff

The same tool, applied to any ontology, produces the same kind of improvement. The intermediate classes emerge from domain knowledge — not from reference to any other implementation.

Further Reading

Tools

43 tools organized by function — available as MCP tools (prefixed onto_) and CLI subcommands:

Architecture

Engine

flowchart TD
    subgraph Clients["Clients"]
        Claude["Claude / LLM\nMCP stdio"]
        CLI["CLI\nonto_* subcommands"]
        Studio["Studio\nHTTP REST"]
    end

    subgraph Server["Open Ontologies Server"]
        direction TB

        subgraph Transport["Transport Layer"]
            MCP_HTTP["MCP Streamable HTTP\n/mcp"]
            REST["REST API\n/api/query · /api/update\n/api/save · /api/load · /api/lineage"]
        end

        subgraph ToolGroups["43 Tools"]
            direction LR
            Core["Core\nvalidate · load · save · clear\nstats · query · diff · lint\nconvert · status"]
            DataPipe["Data Pipeline\nmap · ingest · shacl\nreason · extend · import-schema"]
            Lifecycle["Lifecycle\nplan · apply · lock · drift\nenforce · monitor · lineage"]
            Advanced["Alignment + Clinical\nalign · crosswalk · enrich\nenrich · embed · search · similarity\ndl_explain · dl_check"]
            Version["Versioning\nversion · history · rollback"]
        end

        subgraph Core2["Core Engine"]
            GraphStore["Oxigraph Triple Store\nRDF/OWL in-memory\nSPARQL 1.1"]
            SQLite["SQLite\nlineage events\nversion snapshots\nlint/enforce feedback\nembedding vectors"]
            Reasoner["OWL2-DL Reasoner\nSHOIQ tableaux\nRDFS · OWL-RL"]
            Embedder["Embedding Engine\ntract-onnx (ONNX)\ntext + Poincaré structural"]
        end
    end

    subgraph External["External Sources"]
        PG["PostgreSQL\nschema import"]
        SPARQL["Remote SPARQL\nendpoints"]
        OWL["OWL URLs\nowl:imports chains"]
        Parquet["Parquet / Arrow\nclinical crosswalks\nICD-10 · SNOMED · MeSH"]
        Files["Files\nCSV · JSON · XML\nYAML · XLSX · Parquet"]
    end

    Claude -->|"MCP stdio"| MCP_HTTP
    CLI -->|"subcommands"| MCP_HTTP
    Studio -->|"sessionless"| REST

    MCP_HTTP --> ToolGroups
    REST --> ToolGroups

    ToolGroups --> GraphStore
    ToolGroups --> SQLite
    ToolGroups --> Reasoner
    ToolGroups --> Embedder

    Reasoner --> GraphStore
    Embedder --> SQLite

    DataPipe --> Files
    Advanced --> Parquet
    Core --> OWL
    Core --> SPARQL
    DataPipe --> PG

Studio

flowchart TD
    subgraph UI["React UI (Vite + Tailwind CSS)"]
        Graph["Virtualized Tree\nDOM + virtual scroll"]
        Chat["AI Chat Panel\nZustand store"]
        Inspector["Property Inspector\nInline SPARQL edit"]
        Lineage["Lineage Panel\nAudit trail"]
        Save["Named Save\n⌘S → ~/.open-ontologies/"]
    end

    subgraph Tauri["Tauri 2 Shell (Rust)"]
        IPC["Tauri IPC\ninvoke / event"]
        ChatState["ChatState\nstdin/stdout pipe"]
    end

    subgraph Engine["Engine Sidecar (Rust / Axum)"]
        MCP["/mcp — MCP Streamable HTTP\n43 onto_* tools"]
        REST2["/api/query · /api/update\n/api/save · /api/load-turtle\n/api/stats · /api/lineage"]
        Store["Arc<GraphStore>\nOxigraph"]
        DB["SQLite"]
    end

    subgraph Agent["Agent Sidecar (Node.js)"]
        SDK["Claude Opus 4.6\nAgent SDK"]
        Proto["stdin/stdout JSON protocol"]
    end

    Graph -->|"SPARQL SELECT/UPDATE · REST"| REST2
    Inspector -->|"SPARQL UPDATE · REST"| REST2
    Lineage -->|"GET /api/lineage"| REST2
    Save -->|"POST /api/save"| REST2
    Chat -->|"invoke send_chat_message"| IPC
    IPC --> ChatState
    ChatState -->|"stdin { type: chat }"| Proto
    Proto --> SDK
    SDK -->|"MCP tools/call"| MCP
    SDK -->|"stdout { type: text/tool_call/done }"| Proto
    Proto -->|"Tauri emit agent-message"| Chat
    MCP --> Store
    REST2 --> Store
    Store --> DB

Design decisions

Stack

Documentation

License

MIT