Interested in leveraging AI to transform manufacturing, but not sure what Basic Formal Ontology (BFO) is all about?

Then maybe you should start doing a deep dive. The future of digital twins will intersect with ontology. And results will be transformative. I highlighted this (importance of data ontology) in a report I published in early 2024.

BFO is not the only ontology approach you need to be conversant in, but it is a good start.

Basic Formal Ontology (BFO) plays a crucial role in data management, especially when dealing with complex, multi-domain or multi-system data (like industrial digital twins, biomedical records, or enterprise knowledge graphs).

In modern organizations, data comes from many heterogeneous sources like databases, sensors, APIs, spreadsheets, etc. Each system uses its own labels and data models (e.g., “machine,” “asset,” “equipment,” “unit” might all mean the same thing).

BFO provides a common upper-level vocabulary, i.e, a set of general categories (e.g., object, process, quality, role) that all data types can map to. It divides everything that exists into two overarching categories, continuants and Occurrents.

Continuants are entities that persist and continue to exist even as their attributes change.

Occurrents are processes, events, or changes, i.e, entities that unfold.

While there is a lot more that goes into it, you can step back and imagine how these two simple overarching categories can help make data integration cleaner, queries more interpretable, and lead to increased interoperability.

Here is a simple example. A turbine sensor dataset (industrial IoT) and a maintenance log (enterprise ERP) can both be aligned under BFO.

“Vibration amplitude (dependent continuant) of turbine (independent continuant) participates in maintenance process (occurrent).”

This paper, almost 15 years old now, explores a special subclass of continuants known as realizable dependent continuants.https://https://lnkd.in/gadmbgAv

If you are a data nerd who likes exploring and making connections, explore ontologies and try to imagine ways it can transform the data and analytics landscape in your organization.

Remember that BFO is not the only ontology approach out there. There are other useful ones, like the Provenance ontology, which tracks data derivation and processing history.