A Practitioner's Guide to Taxonomies, Part II

2026-02-03 · Source: Intentional Arrangement · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

This essay, the second in a series, details the construction of taxonomies using simple, portable structures independent of specific encoding standards like SKOS. It focuses on transforming a flat vocabulary into a structured hierarchy by establishing parent-child relationships, writing clear definitions, and managing alternative labels. The core components of this approach include Preferred Labels as authoritative terms, Hierarchical Relationships to organize concepts from broad to narrow, Definitions to disambiguate each concept, and Alternative Labels to capture synonyms and acronyms. This method emphasizes fundamental taxonomy logic, preparing practitioners for various implementations from spreadsheets to content management systems, before introducing more formal linked data principles in Part III.

Key takeaway

For information architects or content strategists building controlled vocabularies, focus first on establishing clear hierarchical relationships and comprehensive definitions. This foundational work ensures your taxonomy is robust and portable across various tools and systems, regardless of whether you eventually adopt formal standards like SKOS. Prioritizing these core components will streamline future implementation and enhance user experience.

Key insights

Taxonomy construction prioritizes fundamental structural logic over specific encoding standards for broad applicability.

Principles

• Structure hierarchies before formalizing with encoding standards.
• Simplicity in taxonomy design enhances portability.
• Definitions clarify concept boundaries.

Method

Build taxonomies using four core components: Preferred Labels, Hierarchical Relationships (parent-child), Definitions, and Alternative Labels (synonyms/acronyms). This can be done in a simple spreadsheet.

In practice

• Organize terms into parent-child hierarchies.
• Define each concept clearly.
• Map user search terms to preferred labels.

Topics

• Taxonomy Construction
• Controlled Vocabulary
• Hierarchical Relationships
• SKOS (Simple Knowledge Organization System)
• Knowledge Graphs

Best for: Data Scientist, Software Engineer, AI Architect

Related on AIssential

• A Practitioner's Guide To Taxonomies, Part I — Taxonomies offer high ROI as low-fidelity semantic structures, enabling context and inference without full ontological complexity.
• Demystifying SKOS for Practitioners: A Practical Guide to Controlled Vocabularies — SKOS provides a standard data model for interoperable knowledge organization systems, crucial for semantic data and information sharing.
• A Practitioner’s Guide to Taxonomies, Part III — SKOS formalizes taxonomies with URIs and rich properties, making them machine-readable and essential for AI systems.
• Structure vs. Concept — Taxonomies capture human concepts for LLMs; ontologies define structural logic for reasoners.
• Ontology Engineering Tradecraft: This Is Ours, Own It — Ontology engineering uniquely focuses on systematic disambiguation to achieve robust interoperability and information quality.
• Is It Time to Retire the Blank Node? — Blank nodes in RDF 1.0 create technical debt; RDF 1.2 features offer superior, globally referenceable alternatives for graph structures.

Open in AIssential →

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