ontology-engineering

name	ontology-engineering
description	Build, validate, and govern RDF/OWL ontologies using the Open Ontologies MCP server. Use when the user asks to create, modify, query, or manage ontologies, knowledge graphs, or RDF data.

Ontology Engineering Workflow

You have access to the Open Ontologies MCP server, which provides 50+ tools for AI-native ontology engineering backed by an in-memory Oxigraph triple store.

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 -- you know OWL, RDF, BORO, 4D modeling natively

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. For repos that mount a folder of .ttl files via [general] ontology_dirs, prefer onto_repo_load so the same compile-cache / TTL-eviction path is exercised. Use onto_repo_list to discover candidate files.
• 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