From Trees to Topologies: The Evolution of Meaning in Systems Engineering
From Trees to Topologies: The Evolution of Meaning in Systems Engineering
Introduction: How We Organize the World
Human beings are obsession-machines when it comes to sorting things. From the moment we invented language, we’ve been trying to put the world into boxes. We categorize animals, we file paperwork, and we organize software code. But the way we organize information has evolved drastically, especially with the rise of modern computing and Artificial Intelligence. To understand how modern systems—like our own matching algorithm—work, we need to understand the evolution of data modeling: from Linguistic Trees and Taxonomies to Ontologies and, finally, to the complex Semantic Topologies that power AI.
These aren't just academic terms for linguists; they are the blueprints for how every search bar, recommendation engine, and database you interact with operates. As Jim McHugh notes, we are witnessing a shift from simple grouping (Taxonomy) to complex relationship mapping (Knowledge Graphs).
1. The Linguistic Tree: Rigidity and Ancestry
Think of a Linguistic Tree like a family tree. It is a strict, hierarchical structure where every item has a single parent. In linguistics, this is how we trace languages back to their roots (e.g., Indo-European → Germanic → West Germanic → English).
In Systems Engineering, trees are the oldest and most rigid form of organization. Your computer’s file system is a tree: a file sits in a folder, which sits in another folder, which sits on the hard drive. Data retrieval in a tree is fast because the path is definite, but it is also brittle. If you misfile a document, it is effectively lost. If a concept belongs to two parents—like a "Tomato" being both a "Fruit" (botanically) and a "Vegetable" (culinarily)—a tree struggles to handle it without duplicating the item.
2. Taxonomies: The Ordered Classification
A Taxonomy is a step up from a tree. While often still hierarchical, taxonomies are designed specifically for classification and tagging. McHugh identifies this as the "broad to narrow" framework. It is about grouping.
In our own platform, we use a . When a coach selects their competencies, they select from a standardized list where "Project Management" might exist under "Business Skills". We enforce standards—like using for unique IDs and specific role-scoping—to keep this clean. Taxonomies allow us to say, "Show me all coaches who know 'React'."
