quickstart.md

Quickstart

Get from zero to a validated, queryable ontology in under 2 minutes.

Install

# 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

# Initialize (creates ~/.open-ontologies with a database and a default config)
open-ontologies init

Connect

Add to your MCP client config (Claude Code: ~/.claude/settings.json):

{
  "mcpServers": {
    "open-ontologies": {
      "command": "open-ontologies",
      "args": ["serve"]
    }
  }
}

Use

Ask Claude:

Build me a Pizza ontology with 5 toppings and 3 named pizzas.
Validate it, load it, and show me the stats.

Claude will call onto_validate -> onto_load -> onto_stats -> onto_lint automatically.

CLI mode

Most MCP tools also work as CLI subcommands. The embedding tools (onto_embed, onto_search, onto_similarity) are MCP only and have no CLI equivalent in any build.

# Validate a Turtle file
open-ontologies validate pizza.ttl

# Load and query
open-ontologies load pizza.ttl
open-ontologies query "SELECT ?class WHERE { ?class a owl:Class }" --pretty

# Run SHIQ tableaux reasoning
open-ontologies reason --profile owl-dl
# ...with a 30 s budget per phase for this run (overrides [reasoner] in config.toml)
open-ontologies reason --profile owl-dl --phase-timeout-ms 30000

# Semantic search (requires a build with --features embeddings)
open-ontologies init  # on an embeddings build, downloads a 448 MB ONNX model and a 16 MB tokenizer
# then via MCP: onto_embed -> onto_search "domestic animal"

What's next

• Data Pipeline -- ingest CSV/JSON/Parquet into your ontology
• Ontology Lifecycle -- plan, enforce, apply, monitor changes
• SHIQ Reasoning -- native Rust SHIQ tableaux, no nominals
• Semantic Embeddings -- dual-space search (text + Poincare)
• Benchmarks -- performance numbers and comparisons