Why Ontologies Were Sidelined—and Why AI Now Makes Them Non‑Optional
Ontologies have been obviously right for decades—and yet persistently sidelined. That paradox is not accidental. It reflects a convergence of historical, economic, organizational, and epistemic forces that shaped modern data management in ways fundamentally misaligned with ontology-driven thinking.
Below is a clear, candid diagnosis—distinguishing what happened, why it stuck, and why that resistance is now breaking.
1. Ontologies Arrived Too Early—and Too Abstract
Ontologies matured intellectually before enterprises were ready operationally.
The modern ontology movement emerged from:
● Philosophy (formal semantics, metaphysics)
● AI research (knowledge representation)
● Semantic Web initiatives (RDF, OWL)
These communities are optimized for correctness and expressiveness, not adoption.
Ontologies asked organizations to agree on meaning before extracting value—a hard sell in environments rewarded for speed and delivery.
Early ontology tools:
● Required specialized training
● Lacked intuitive UX
● Were disconnected from business workflows
Result: Ontologies were perceived as academic artifacts rather than practical infrastructure.
2. The Relational Database Won—And Froze the Paradigm
Relational databases didn’t just dominate—they defined what data management meant.
Codd’s relational model (1970):
● Prioritized structure, normalization, and efficiency
● Explicitly avoided semantics beyond schema constraints
● Encouraged a separation between data storage and meaning
Schemas became:
● Physical artifacts
● Application-specific
● Owned by IT, not the business
Once SQL became the lingua franca, everything else was forced to adapt to it—including ontologies, which were treated as overlays rather than foundations.
Meaning was externalized into code, documentation, and tribal knowledge—where it remains today.
This architectural choice created 40 years of technical and organizational inertia.
3. Ontologies Threaten Power Structures
Ontologies don’t just model data. They model agreement.
That makes them politically dangerous.
Ontology-driven data management requires:
● Cross-domain consensus
● Shared definitions
● Explicit ownership of meaning
This challenges:
● Application silos
● Local autonomy
● “My data, my rules” cultures
Many data leaders discovered—often unconsciously—that ontologies expose contradictions:
● Same term, different meanings
● Different terms, same concept
● Metrics that don’t reconcile across systems
Ontologies surface organizational misalignment that many enterprises would rather tolerate than confront.
Avoidance was easier than resolution.
4. Vendors Had No Incentive to Lead With Ontologies
The enterprise software economy optimized for transactions, not truth.
Major vendors built businesses on:
● Databases
● ERPs
● BI tools
● Data lakes and warehouses
Ontology-first systems would have:
● Reduced integration friction
● Exposed semantic inconsistencies
● Undermined the need for endless ETL, mapping, and reconciliation projects
In short: ontologies were bad for billable complexity.
Even Semantic Web tooling was often positioned as:
● Niche
● Experimental
● “For metadata,” not core data flows
Without commercial champions, ontologies stayed marginal.
5. The Rise of “Big Data” Made Things Worse—Temporarily
Big Data inverted the problem—and delayed the reckoning.
The dominant narrative became:
“Don’t model. Just collect everything and figure it out later.”
Data lakes:
● Deferred semantics
● Encouraged schema-on-read
● Amplified ambiguity
This worked—until AI arrived.
Machine learning systems exposed a brutal truth:
● Models don’t fail because of insufficient volume
● They fail because of inconsistent meaning
Garbage in, confidently scaled garbage out.
6. Ontologies Were Framed as “Metadata”—A Fatal Undersell
Perhaps the most damaging mistake was linguistic.
Ontologies were:
● Labeled as metadata
● Assigned to governance teams
● Decoupled from operational systems
This positioned them as:
● Passive
● After-the-fact
● Compliance-oriented
In reality, ontologies are:
● Executable semantics
● Operational context
● The control plane for data and AI
But by the time that distinction became clear, the damage was done.
7. Why This Is Changing—Now
The resistance is collapsing under three forces:
1. AI Systems Demand Semantic Coherence
LLMs, agents, and autonomous systems require:
● Stable concepts
● Explicit relationships
● Traceable meaning
Statistical inference alone is insufficient.
2. Regulation Requires Explainability and Lineage
EU AI Act, ISO/IEC 42001, and emerging data regulations require:
● Traceability
● Accountability
● Consistent definitions
You cannot govern what you cannot define.
3. Data Has Become a Strategic Asset—Not an IT Artifact
Executives now recognize that:
● Data quality is a business risk
● Semantic drift destroys enterprise value
● AI without shared meaning is organizational self-harm
Ontologies are no longer optional—they are overdue.
A Provocative Reframe
Ontologies weren’t slow to be accepted because they were wrong.
They were slow because they demanded honesty—about meaning, ownership, and alignment—before enterprises were ready to confront it.
AI is forcing that confrontation.
Sources (in order of appearance)
Gruber, T. R., A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, 1993.
Uschold, M., & Grüninger, M., Ontologies: Principles, Methods and Applications, Knowledge Engineering Review, 1996.
Codd, E. F., A Relational Model of Data for Large Shared Data Banks, Communications of the ACM, 1970.
Date, C. J., An Introduction to Database Systems, Addison-Wesley, 2003.
Pollock, J. T., Semantic Enterprise Information Management, Wiley, 2012.
Gartner, Metadata Management Solutions, 2018.
Halevy, A., Norvig, P., Pereira, F., The Unreasonable Effectiveness of Data, IEEE Intelligent Systems, 2009.
Sambasivan, N. et al., Everyone Wants to Do the Model Work, Not the Data Work, ACM CHI, 2021.
Bender, E. M. et al., On the Dangers of Stochastic Parrots, FAccT, 2021.