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STIDS 2026: Ontology, AI, and the Return of Serious Semantic Engineering

· One min read
John Beverley
John Beverley
President, National Center for Ontological Research

Event Report

STIDS 2026 brought the ontology community back into the room.

With approximately 150 registered attendees across in-person and remote participation, STIDS 2026 showed that semantic technology is no longer a niche academic concern. It is becoming central to AI, defense, intelligence, standards, and data interoperability.

STIDS 2026 made one thing clear: the future of AI depends on more than larger models and larger datasets. It depends on better representations, better governance, and better conceptual clarity.

NCOR takeaway

The organizations that win in AI will be the ones that know what their data means.

Highlights​

  • Strong participation from government, industry, and academic researchers.
  • Serious discussion of ontology engineering as infrastructure for AI.
  • Increased attention to knowledge graphs, semantic interoperability, and data quality.
  • Productive overlap with KGOIDS and related defense and intelligence communities.
  • Renewed interest in NCOR as a hub for ontology best practices.

What comes next​

NCOR will continue building the infrastructure around ontology education, certification, best practices, and community coordination. STIDS 2026 was not just an event. It was a signal that this field is entering a new phase.

Moving Data Is Not the Same as Preserving Meaning

· 6 min read
John Beverley
John Beverley
President, National Center for Ontological Research

Meaning Matters · Part 1

Moving Data Is Not the Same as Preserving Meaning

Data integration can move information from one system to another. Semantic integration makes sure the meaning survives the trip.

Core claim

Interoperability problems are not simply about whether your organization can access data, but whether it can recover, test, and trust the same meaning after the data has moved.

Most organizations think they have an interoperability problem.

They have too many systems. Too many dashboards. Too many databases. Too many teams using different words for similar things and the same words for different things. So they buy a platform, build APIs, export data, create pipelines, and declare victory when the data finally moves from one place to another.

But moving data is not the same as preserving meaning.

A spreadsheet can be exported. A JSON object can be passed through an API. A table can be replicated from one system to another. None of that guarantees that the receiving system understands what the data means.

Take something as ordinary as location.

In one system, location might mean where an object was observed. In another, where it is assigned. In another, its last known position. In another, its expected destination. In another, a region associated with responsibility, ownership, service coverage, or responsibility.

Same field name

location

Observed locationAssigned locationLast known positionExpected destinationService coverage region

The field name is the same. The meaning is not.