ontobdc 0.6.3

OntoBDC (Ontology-Based Data Capabilities)

OntoBDC

OntoBDC is a domain-driven data architecture for engineering systems.

OntoBDC provides a structured way to define capabilities, actions, and use cases over engineering data.

It enables reproducible, auditable, and automation-ready workflows across technical domains.

🏗️ Used by

OntoBDC is currently used as the core data and execution layer of:

• InfoBIM

InfoBIM leverages OntoBDC to define capabilities, execute checks, and orchestrate engineering data workflows.

📦 Requirements

• Python 3.11+
• pip

🚀 Getting Started

Install the package:

pip install ontobdc

After installation, the ontobdc CLI becomes available:

ontobdc --version

Initialize the project to create the local configuration:

ontobdc init

The init command automatically detects the environment (e.g., venv or Google Colab) and creates the .__ontobdc__ directory with the base configuration:

Execute capabilities interactively:

ontobdc run

This command launches an interactive menu to discover and execute available capabilities:

From there, you can run other capabilities, perform actions, and interact with registered use cases.

Alternative: Google Colab

You can try OntoBDC directly in Google Colab without installing anything locally.

View or download the example notebook to see capabilities in action.

✅ Checking

The check command validates engineering data against defined capabilities and rules.

ontobdc check --repair

It executes registered checks over the target dataset, reports inconsistencies, and optionally applies automated repairs when --repair is enabled.

This ensures reproducibility, auditability, and deterministic validation of engineering workflows.

🤝 Contributing

We are always on the lookout for contributors to help us fix bugs, create new features, or help us improve project documentation. If you are interested, feel free to create a PR or open an issue on this topic.

📄 License

Licensed under Apache 2.0.