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6 posts tagged with "Standards"

Standards, governance, certification, and best practices

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The World’s Ontology Ecosystem, Day 5: Where Ontology Engineering Goes Next

· 8 min read
John Beverley
John Beverley
President, National Center for Ontological Research
Event Report · The World’s Ontology Ecosystem · Day 5

Where ontology engineering goes next

The final day synthesized the week through participant presentations, capstone work, ontology mapping, formal verification, AI, tooling, education, and competing visions for the future of the field.

Guiding question
What should the next generation of ontology engineering become?
Core claim

The next generation of ontology engineering will be defined less by building isolated ontologies and more by governing mappings, connecting formal and statistical reasoning, integrating ontology with verification and AI workflows, improving tooling, and training enough practitioners to make semantic infrastructure operational at scale.

The World’s Ontology Ecosystem, Day 4: Building AI Workflows You Can Actually Validate

· 6 min read
John Beverley
John Beverley
President, National Center for Ontological Research
Event Report · The World’s Ontology Ecosystem · Day 4

Building AI workflows you can actually validate

Day 4 moved from critique to construction: when ontology is the right engineering choice, how agents can use semantic structure, and how reasoning, SHACL, SPARQL, and reproducible evidence can constrain probabilistic AI.

Guiding question
What does a trustworthy ontology-enabled AI workflow actually look like?
Core claim

AI workflows become more trustworthy when probabilistic outputs are surrounded by explicit semantics, deterministic tools, formal tests, provenance, and reproducible evidence. Ontology is most valuable not as decoration around an LLM, but as part of the control structure of the system.

The World’s Ontology Ecosystem, Day 2: Careful Modeling Is the Point

· 6 min read
John Beverley
John Beverley
President, National Center for Ontological Research
Event Report · The World’s Ontology Ecosystem · Day 2

Careful modeling is the point

Day 2 moved from the foundations of ontology engineering to difficult modeling practice: unreal and fictional subject matter, law, systems, definitions, design patterns, OWL, and reasoning.

Guiding question
What does competent ontology engineering look like when the easy modeling choices stop working?
Core claim

Good ontology engineering is not a mechanical translation from nouns to classes and verbs to relations. It requires disambiguation, explicit ontological commitments, defensible definitions, reusable design patterns, and formal tests of what follows from the model.

The World’s Ontology Ecosystem, Day 1: From Philosophy to Tradecraft

· 6 min read
John Beverley
John Beverley
President, National Center for Ontological Research
Event Report · The World’s Ontology Ecosystem · Day 1

From philosophy to tradecraft

The opening day established the foundations of applied ontology: where the field came from, why shared semantic infrastructure matters, and what distinguishes ontology engineering as a professional tradecraft.

Guiding question
What kind of field is applied ontology, and what does someone need to know to practice it well?
Core claim

Ontology engineering is not simply the production of class hierarchies or knowledge graphs. It is a discipline for making distinctions explicit, preserving meaning across systems, improving information quality, and coordinating communities around reusable semantic commitments.

Lossy, Never Divergent: The Rule Every Semantic Architecture Needs

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

Semantic Infrastructure · Part 2

Lossy, Never Divergent

Operational systems can simplify meaning for performance, usability, and exchange. But they must not contradict, redefine, or silently alter the governed semantic model.

Core claim

A derived product may omit semantic detail when the target format cannot faithfully carry it. But omission is not permission to redefine meaning. The rule is simple: lossy is sometimes acceptable; divergent is not.

Every serious data architecture produces derived products.

An ontology may be projected into a schema. A semantic model may be mapped into a property graph. A governed vocabulary may appear inside an API. A relation may be implemented through code. A validation rule may become a SHACL profile, a database constraint, or an application check. A model may be transformed into JSON, tables, dashboards, workflow objects, vector indexes, or AI-ready data products.

This is normal.

No serious architecture should expect every downstream system to carry every semantic commitment in its richest form.

But that does not mean downstream systems can silently change what things mean.

That is where the rule matters.

The rule

Lossy, never divergent.

A derived product may omit semantic detail when the target format cannot faithfully carry it.

It must not contradict, redefine, alter, or silently deviate from the authoritative semantic model.

Open Standards Keep Meaning Portable

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

Meaning Matters · Part 2

Open Standards Keep Meaning Portable

Open semantic standards are not nostalgia. They are a way to keep meaning visible, inspectable, testable, and independent of any one platform.

Core claim

A data platform helps you manage data. A semantic standard helps you govern what the data means. Confuse those two roles, and organizations risk surrendering semantic independence.

Every few years, someone declares that open semantic standards are obsolete.

The argument usually sounds practical. The market has moved on. Developers prefer simpler formats. Operational platforms need speed and scale. Business users need dashboards, workflows, and applications, not formal models.

There is a grain of truth to this.

Operational platforms should not be judged only by whether they use a semantic standard as their native runtime architecture. Serious systems are layered. They combine SQL, JSON, APIs, graph stores, search indexes, workflow engines, code, and user interfaces.

No one should expect one standard to do every job.

But that does not mean open semantic standards are irrelevant. It means we need to understand what job they are supposed to do.

Open semantic standards give organizations a transparent, inspectable, machine-readable way to represent shared meaning.