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fabio-rovai/open-ontologies

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AI-native ontology engine: a Rust MCP server with tools for building, validating, querying, and reasoning over RDF/OWL ontologies. In-memory Oxigraph triple store, native OWL2-DL t

F

MCP

fabio-rovai/open-ontologies

Added 1 June 2026

#ai-native #claude #description-logics #knowledge-graph #linked-data #mcp #mcp-server #ontology

Overview

Open-ontologies is a Rust MCP server for building, validating, querying, and reasoning over RDF/OWL ontologies. It uses an in-memory Oxigraph triple store with a native OWL2-DL tableaux reasoner, SHACL validation, and SPARQL support. The single binary runs without a JVM.

Best for

Best for
Rust developers needing a fast, self-contained ontology engine for embedded or server-side use

Use cases

  • Embedding ontology reasoning into Rust applications
  • Validating RDF data against SHACL shapes
  • Running SPARQL queries on in-memory ontologies

Notes

Open-ontologies is a Rust MCP server for building, validating, querying, and reasoning over RDF/OWL ontologies. It uses an in-memory Oxigraph triple store with a native OWL2-DL tableaux reasoner, SHACL validation, and SPARQL support. The single binary runs without a JVM.

126 stars on GitHub. Last updated 2026-05-29. Licensed MIT.

Use cases

  • Embedding ontology reasoning into Rust applications
  • Validating RDF data against SHACL shapes
  • Running SPARQL queries on in-memory ontologies

Pros

  • Lightweight single binary with no JVM dependency
  • Native OWL2-DL reasoning and SHACL validation built-in
  • Simple MCP server interface for integration

Cons

  • In-memory store limits ontology size to available RAM
  • No built-in persistence or distributed query capabilities
  • Smaller community and fewer integrations than Java-based alternatives

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Lightweight single binary with no JVM dependency
  • Native OWL2-DL reasoning and SHACL validation built-in
  • Simple MCP server interface for integration

Cons

  • In-memory store limits ontology size to available RAM
  • No built-in persistence or distributed query capabilities
  • Smaller community and fewer integrations than Java-based alternatives