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Open Ontologies

by fabio-rovai

Plans, validates, queries, and verifies OWL/RDF knowledge-graph changes through 39 MCP tools and five prompts.

Install Open Ontologies

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Overview

fabio-rovai/open-ontologies exposes ontology engineering workflows through MCP, using an Oxigraph triple store for OWL, RDF, and SPARQL data. Its 39 tools and five prompts cover validation, querying, diffing, linting, versioning, governance, and semantic change planning. The Rust implementation ships as a single binary and can produce certificates that another person can verify without the software or network access. Reach for it when ontology changes need consequence analysis, rule-based reasoning, or independently checkable evidence before production.

Use cases

  • Plan ontology changes before production deployment
  • Validate OWL and RDF knowledge graphs
  • Query graph data with SPARQL
  • Review inferred consequences of semantic changes
  • Verify derivation certificates independently

Key features

  • 39 MCP tools and five prompts
  • Oxigraph-backed OWL/RDF/SPARQL workflows
  • Semantic diff and change-impact planning
  • Rule-based inference with consequence reporting
  • Certificate generation and offline verification
  • Single Rust binary

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

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Adoption & maintenance

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GitHub stars1.7kStargazers on the source repository.
Last commit1d agoMost recent push to the default branch.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Open Ontologies works

What fabio-rovai/open-ontologies does

fabio-rovai/open-ontologies brings ontology engineering operations into MCP clients. It works with OWL and RDF knowledge graphs and supports SPARQL-oriented querying through an Oxigraph triple store. The project provides 39 tools and five prompts for inspecting, changing, checking, and governing ontology data.

Its focus is change analysis rather than visual ontology editing. A proposed change can be evaluated for added and removed classes, affected triples, risk, conservativity, and newly derived consequences. This makes it suitable for reviewing a semantic change before applying it to a production graph. Protégé remains the appropriate tool for drawing class hierarchies; Open Ontologies evaluates the change around that workflow.

How it works

The engine is written in Rust and distributed as one binary. It communicates with AI applications through MCP, including Claude and Cursor. Ontology content is processed through an Oxigraph-backed triple store, where the server can validate data, execute queries, compare versions, and apply reasoning rules.

A batch interface can read commands such as load base.ttl and plan proposed.ttl. Planning compares the proposed ontology with the loaded base and reports semantic effects that a text diff would not reveal. For example, a change that adds one domain assertion can leave the structural blast radius at zero while still producing hundreds of new inferred consequences under the OWL-RL rule table.

The project also supports proof-oriented review. A run can write asserted triples and derivations to files, while oo-cert asserted.tsv derivations.tsv checks the resulting certificate. The checker accepts valid derivations and rejects forged conclusions, identifying the rule that fails. Verification can be performed later without an installation of the engine or a network connection.

Setup and configuration

Run the distributed Open Ontologies binary and connect it to an MCP client that supports the protocol. The project is designed to operate without a JVM or Protégé. Claude and Cursor are named client targets, and the MCP interface is intended for other compatible clients as well.

For command-line workflows, the batch command accepts input from standard input. A typical planning flow loads a base Turtle file and then plans a proposed file:

text
load base.ttl
plan proposed.ttl

Certificate verification uses the separate oo-cert command with the asserted-triple and derivation files as arguments. The engine and checker are MIT-licensed.

Tools and capabilities

  • Validate OWL and RDF ontology content.
  • Query graph data with SPARQL-oriented operations.
  • Compare ontology versions and inspect semantic diffs.
  • Lint ontology structure and identify modeling problems.
  • Plan proposed changes and calculate their consequences.
  • Apply rule-based reasoning and report inferred triples.
  • Create and independently verify derivation certificates.
  • Support ontology versioning and governance workflows through MCP tools and prompts.

The project’s examples include checks for undeclared class terms, invalid rdfs:range usage, near-duplicate names, satisfiability, and rejected forged derivations. These checks help distinguish a syntactically small change from one that alters the graph’s inferred meaning.

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Frequently Asked Questions about Open Ontologies

fabio-rovai/open-ontologies is an MCP server that connects AI clients to OWL and RDF ontology workflows backed by an Oxigraph triple store. Its main tools validate, query, diff, lint, version, govern, and plan knowledge-graph changes, while reasoning and certificate tools expose inferred consequences and independently verifiable derivations.

Install and run the distributed single binary, then connect it to an MCP client. The project is written in Rust and does not require a JVM or Protégé. Command-line workflows can use the binary’s batch interface, while certificate checking uses the `oo-cert` command.

Yes, Cursor is one of the MCP clients named for fabio-rovai/open-ontologies. Connect the server’s single binary through Cursor’s MCP configuration, then use its ontology tools and prompts for validation, querying, semantic diffs, reasoning, and change planning.

Yes, it can generate derivation certificates and check them with `oo-cert`. The checker accepts valid derivations and rejects forged conclusions, naming the rule that does not hold. Verification can happen later without the engine installation or network access.

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