Open Ontologies

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

English · 简体中文

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

Open Ontologies is a Rust MCP server and desktop Studio for AI-native ontology engineering. It exposes 70+ tools that let Claude build, validate, query, diff, lint, version, reason over, align, plan, certify, and govern RDF/OWL ontologies using an in-memory Oxigraph triple store — with a full three-layer Dynamics → Causal → Planner architecture, a marketplace of 33 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.

What's New (three-layer architecture + 13 new primitives)

The full Dynamics → Causal → Planner stack plus 13 new primitives. Every piece holds the MCP-native convention: the server provides validation and scaffolding, the connected LLM (Claude over MCP) does the intelligence. No internal LLM clients, no API keys, no provider abstractions.

Three-layer architecture

13 new primitives

• onto_owl_shacl_coevolve_check + onto_owl_shacl_coevolve_incremental — SHACL validation against the OWL-RL closure, with dependency-graph routing so only shapes touching changed IRIs revalidate.
• onto_segment_retrieve — TBox-slice retrieval for ontology-grounded RAG.
• onto_extract_scaffold + onto_extract_validate — schema-guided structured extraction with typed datatype validation + conformance scoring.
• onto_cq_run + onto_verify_cq + onto_cq_verdicts_list — competency-question runner with pitfall hints + LLM-judgement loop.
• onto_classify_el — OWL-EL classification (transitive subsumption table, trivial pairs excluded).
• onto_eval_alignment — P/R/F1 over reference + computed alignment sets.
• onto_shape_combinatorics + onto_shape_induce — property-combination lattice + data-driven SHACL shape induction with support × confidence ranking.
• borderline_partition + borderline_record_verdict — generalised two-threshold review loop for any candidate set.
• onto_align_fuzzy — embedding-free fuzzy-logic adjudication with 10-rule Mamdani inference; HNSW is demoted to a candidate generator.
• onto_align_flora — end-to-end alignment pipeline pairing the signal extractor to the fuzzy adjudicator.
• onto_policy_register + onto_policy_list + onto_policy_check — authorisation gate that composes with onto_certify_action (Causal = risk; policy = authorisation).
• eval_rag + eval_rag_mmrag — Hit@k / MRR / faithfulness / token-Jaccard / ROUGE-1 scoring for retriever pipelines, with a dataset adapter.
• graph_projection_lossy_check — the auditor that pairs with onto_segment_retrieve.

Validating end-to-end

cargo run --example three_layer_pipeline

Walks Dynamics register → PDDL compile → Fast-Downward-shaped sas_plan parse → orchestrator-side IRI bind → sandbox validate → CIVeX certify → apply with OWL-RL ramification → final state inspection. Every layer through its public API, no external dependencies (Python, DoWhy, Fast Downward) required.

Zero new external Rust dependencies; everything optional gates behind Cargo features. Full test suite (160+ tests) green on default build; cargo clippy --lib --tests --examples -- -D warnings clean across both default and causal-pywhy configurations.

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

serve starts an MCP server that speaks JSON-RPC over stdin/stdout — it is not an interactive CLI, so on launch it will appear to "hang" while it waits for an MCP client to connect. That is expected. To try the tools directly from a terminal instead, use the CLI subcommands (e.g. open-ontologies validate <file.ttl>); to use it with an LLM, wire it into an MCP client as shown under Connect to your MCP client.

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

For native Windows builds, see docs/windows.md.

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"]
    }
  }
}