Before the Structure

2026-06-01 · Source: Intentional Arrangement · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, long

Summary

The article, "Before the Structure," emphasizes that effective knowledge systems, especially for AI applications and knowledge graphs, require a foundational "discipline of organizing" that precedes technical implementation. Drawing on the work of Robert Glushko and Elaine Svenonius, it defines an organizing system by its intentional arrangement of resources to support interactions. The author contends that many AI initiatives fail by prematurely adopting solutions like knowledge graphs without first addressing six critical design decisions: what, why, how much, when, how/by whom, and where resources are organized. It highlights that a knowledge graph is the final layer in a progressive stack of organizing systems, starting from basic term lists and advancing through taxonomies, thesauri, and ontologies, as outlined by Gail Hodge. The piece also underscores the necessity of externalizing tacit knowledge before any structure can be effectively applied.

Key takeaway

For AI Architects and Semantic Engineers designing knowledge infrastructures, prioritize foundational organizing principles over immediate platform acquisition. Your success hinges on deliberately answering Glushko's six design questions—what, why, how much, when, how/by whom, and where resources are organized—before implementing any technology. Skipping these conceptual steps leads to systems that lack inherent meaning, making your knowledge graphs ineffective and models prone to failure. Focus on defining resource identity and codifying tacit knowledge first.

Key insights

The discipline of organizing, rooted in intentional design decisions, must precede any knowledge system's technical structure.

Principles

• Organizing systems require intentional arrangement by an agent.
• Resource identification precedes description and organization.
• Purpose dictates the degree and method of organization.

Method

The "Ontology Pipeline™" proposes an iterative build: controlled vocabulary, metadata schemas, taxonomy, thesaurus, ontology, then knowledge graph. Each layer depends on the one beneath it.

In practice

• Answer Glushko's six design questions before building.
• Define resource identity (unit, description, persistence).
• Prioritize codifying tacit knowledge before system design.

Topics

• Knowledge Organization
• Knowledge Graphs
• Semantic Systems
• Information Architecture
• Ontology Pipeline™
• Tacit Knowledge Management

Best for: AI Architect, AI Engineer, AI Student

Related on AIssential

• Systems for Organizing — Effective AI requires structured knowledge organization systems mirroring human cognition, not just statistical approximations.
• Why Early Knowledge Graph Adopters Will Win the AI Race — Early adoption of knowledge graphs and ontologies provides a competitive edge in enterprise AI.
• Intentional Arrangement — Structured semantic data is fundamental for organizing information, enabling effective digital environments and advanced AI capabilities.
• The Semantic Infrastructure Opportunity: Building Meaningful Operational Frameworks — Semantic infrastructure, especially entity resolution, makes ontology work measurably operational and critical for AI systems.
• Build Meaning Before Machines: Why Semantics, Ontologies, And Knowledge Graphs Matter For Agentic AI — Agentic AI requires explicit data meaning, making semantic layers and knowledge graphs essential architectural components.
• The Ontology Pipeline™, Refresh — Effective semantic knowledge infrastructure requires a structured pipeline, human expertise, and robust governance, not just AI-generated outputs.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Intentional Arrangement.