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Tutorials

These executable notebooks form a guided path from local inference to richer, real-world reasoning workflows.

  • 1. RDFLib graphs with pyling

    Establish the RDFLib-in, RDFLib-out integration pattern.

    Start the tutorial

  • 2. OWL 2 RL materialization

    Replace a toy rule with a maintained semantic profile.

    Materialize OWL rules

  • 3. Neuro-symbolic validation

    Turn uncertain extracted facts into an auditable review queue.

    Validate extracted facts

  • 4. QUDT message logs

    Reason over independently scoped streaming measurements.

    Process message logs

  • 5. ODRL FORCE compliance

    Combine policy, request, and world state into a linked compliance report.

    Evaluate a policy

The documentation build executes every notebook before Material for MkDocs renders it. This keeps the displayed outputs tested and makes each example copyable as Python API documentation.

Run locally

Install the documentation dependencies and open the notebooks:

python -m pip install -e ".[docs]"
jupyter lab docs/notebooks

Or execute all notebooks in place:

jupyter nbconvert --to notebook --execute --inplace docs/notebooks/*.ipynb

Network access

Tutorials 2 and 4 load maintained rule profiles and fixtures from the pietercolpaert/rdfjs-inference-engine repository on GitHub. Executing the complete set therefore requires network access; the source URLs are visible in the notebooks.