Organizations invest months — sometimes years — developing ontologies, only to discover the result isn't practically viable: too rigid, too generic, or too disconnected from how the operation actually runs. The problem isn't that ontologies don't matter. It's that the way we build them is broken.
/ 01What is an ontology, and how is it used?
An ontology is a formal representation of knowledge within a domain: a shared vocabulary for concepts and the connections between them. Think of it as the blueprint for how information is organized, linked, and interpreted.
Done well, an ontology enables semantic search, AI reasoning, and interoperability between systems that were never designed to talk to each other. It turns disconnected information into a unified knowledge environment — and with it comes clarity, consistency, and better decisions. That makes a good ontology a strategic asset, not a technical artifact.
/ 02Why ontologies are hard to develop
The fundamental struggle is knowing where to begin. At the core is a gap between two kinds of expertise: engineers hold the technical skills but lack the nuanced domain knowledge to model the real world accurately; subject-matter experts understand the operation deeply but have no training in formal frameworks like BFO, CCO, or CCV. Between plain-language descriptions and structured logic, communication breaks down.
Two obstacles compound the problem:
- Starting from raw, unstructured data with no clear methodology for extracting the concepts that matter.
- Forcing operational data into rigid, predefined frameworks that don't align with what the organization actually needs.
Ontologies are hard not because of technical complexity, but because of their inherently interdisciplinary nature — they must bridge machines, humans, data, and decision-makers.
/ 03A better way forward
The traditional linear approach — specify, model, deploy, discover it's wrong — should be abandoned in favor of something adaptive, collaborative, and automated. Three shifts make the difference:
- Empower subject-matter experts with intuitive tools. Replace formal languages with platforms SMEs can contribute to directly. Feedback loops shorten, and real-world alignment is built in from day one.
- Leverage automation for discovery and structuring. Machines are better at spotting patterns across complex datasets. Let automated systems generate candidate ontologies and surface relationships — humans validate and refine.
- Treat ontologies as living artifacts. Trade perfectionism for continuous improvement: launch a minimal viable ontology, validate it operationally, iterate on feedback.
Aktiver automatically creates and refines taxonomies and ontologies from disparate data sources, with human-in-the-loop tools for the experts who know the domain best. If your organization has felt the cost of slow, mismatched, or overengineered ontologies — we'd love to show you a better path.