Ontology Concepts
This document provides a comprehensive technical reference for how SBE uses ontologies to power a hub-and-spoke digital engineering platform. It explains SBE’s ontology authoring model, management and governance practices, standards compliance, AI/ML considerations, and import/export procedures. The intended audience includes digital thread specialists, systems engineers, architects, integrators, and administrators responsible for designing and governing model-based enterprise systems.
Introduction
An ontology is a shared map of the important things in a domain and how they relate, written in a way that both people and computers can understand. An ontology, as used in SBE, is a governed, machine-interpretable specification of classes (types of things), properties (attributes or measurements), and relations (links between things) that captures domain meaning precisely and unambiguously. Ontologies allow engineers to describe the world in terms that software, analytics, and AI can understand—across tools, domains, and roles.
Ontologies differ from schemas or taxonomies by making relationships first-class citizens, enabling reasoning and constraint checking, and supporting multi-domain integration at scale. They are agnostic to storage format: the same ontology can be realized in graph databases, JSON document stores, or even relational systems.
Modern digital engineering programs coordinate requirements, architecture, test evidence, logistics, and operational context across many disconnected systems. Without a shared semantic foundation, teams face two main problems: (1) conflicting meanings between tools, and (2) a combinatorial explosion of pairwise integrations. Ontologies, placed at the center of a hub-and-spoke integration model, solve both problems. They become the shared language across the enterprise, reducing friction, improving trust, and enabling automated analysis and traceability.
Why Ontologies are Essential for Digital Engineering
Modern digital engineering environments demand more than just data exchange—they demand semantic alignment. Tools differ in how they name things, structure their data, and define relationships. Left unchecked, these differences create ambiguity, integration fragility, and operational inefficiencies that only grow as systems scale.
The root cause of this semantic problem is that every tool in an ecosystem—DOORS, Cameo, JIRA, Teamcenter—has its own internal vocabulary. One tool’s “requirement” may be another’s “stakeholder need.” One tool calls it “component,” another says “block.” These mismatches make it difficult to trace, reason about, or verify cross-cutting concerns. Ontologies provide a shared, governed vocabulary that spans tools, domains, and stakeholders.
The Pairwise Mapping Problem
Without a central semantic model, integrating n systems requires n(n–1)/2 direct connections—an approach that doesn’t scale. Worse, each connection breaks if any endpoint changes. By contrast, an ontology-centered hub-and-spoke architecture reduces integration complexity to O(n), and isolates system-specific changes from the shared core.
Why the Ontology Must Be the Hub
Other candidates—relational schemas, JSON-based APIs, federated search indexes—can describe structure, but they do not preserve or enforce meaning. An ontology explicitly defines what each thing is, how it relates to other things, and what constraints govern those relationships. This makes it ideal as the “semantic DMZ” between tools.
SBE's ontology hub:
Unifies semantics across the digital thread
Enables powerful, cross-tool queries and dashboards
Drives reasoning, validation, and AI applications
Provides a change-tolerant foundation for adapters and integrations
As the transcript of the ontology training session explains:
“You can’t just rely on metadata or naming conventions. You need something that actually models the meaning—and that’s the ontology. It becomes the reference model for every channel.”
This is why every digital thread in SBE is tied to an ontology context—a versioned snapshot of meaning that tools, queries, and reasoning engines can rely on with confidence.