From Concepts to Semantics — Introducing SKOS

Overview

Questions

• How do we move from lists of terms and definitions to formal, machine-readable vocabularies?
• What does it mean to give a term a URI and define its relationships to others?
• How can SKOS help represent our concepts and mappings in a structured, shareable way?
• How do hierarchical relationships (“broader”, “narrower”, “related”) clarify meaning and enable interoperability?

Objectives

• Explain the purpose of SKOS in representing controlled vocabularies.
• Map existing terms and definitions from a data dictionary into basic SKOS structure (Concept, prefLabel, definition, broader, narrower, related).
• Understand how SKOS differs from an ontology but connects to it (conceptual bridge).
• Create a simple schema diagram showing relationships among terms, using SKOS-like semantics.

Introduction

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Learners have already identified and documented terms (Modules 1–3), and developed competency questions (Module 4). This module introduces semantic structure: how to move from “terms and mappings” to “concepts and relationships” that can be shared, reused, and machine-readable.

This is the first dip into ontology thinking, using SKOS because it’s lightweight, visual, and flexible.

SKOS (Simple Knowledge Organization System) provides a lightweight, flexible way to express controlled vocabularies and their relationships using the Semantic Web.

SKOS Term	Meaning	Example (Salmon Context)
skos:Concept	A unique concept or term	“Smolt condition factor”
skos:prefLabel	The preferred human-readable label	“Condition factor”
skos:definition	Text definition of the concept	“A measure of body condition calculated as weight/length³”
skos:broader	More general concept	“Smolt condition factor” broader: “Condition metric”
skos:narrower	More specific concept	“Smolt condition factor” narrower: “Fork length condition factor”
skos:related	Related but not hierarchical concept	“Condition factor” related to: “Smolt age”
skos:exactMatch, skos:closeMatch	Crosswalk to another vocabulary	“Condition factor” exactMatch: https://vocab.nerc.ac.uk/condition_factor/

SKOS helps structure your data terms before you build an ontology — it’s a bridge between documentation and formal reasoning.

Discussion

Challenge 1: From Data Dictionary to SKOS (25 min)

Purpose: Practice turning natural-language data terms into formal SKOS concepts.

Instructions:

1. Take 3–5 terms from your data dictionary (Modules 1–3).
2. For each term, fill in:

• Preferred label
• Definition (or short description)
• Broader / narrower / related concepts (if applicable)
• Equivalent or similar terms in another dataset or vocabulary

3. Assign a temporary URI (e.g., https://example.org/salmon/condition_factor).

4. Note which relationships are uncertain or need discussion.

🧠 Tip: You don’t need RDF syntax yet — the goal is concept structure, not code.

Show details

Concept	PrefLabel	Definition	Broader	Related URI
Smolt condition factor	Condition factor	Weight/length³, used as an indicator of fish health Condition metric	Smolt length	https://vocab.salmon.org/SmoltConditionFactor

Discussion

Challenge 2: Build a Simple Schema Diagram (20 min)

Purpose: Visualize how your SKOS concepts relate to one another.

Instructions:

1. On a whiteboard or digital diagram tool (e.g., MS PowerPoint, Google Slides, MS Paint, paper):

• Draw boxes for each concept.
• Connect them with arrows labeled broader, narrower, or related.

2. Check:

• Is the hierarchy logical (no circular relationships)?
• Are broader/narrower concepts consistent in scope?
• Where could you reuse existing concepts from other vocabularies?

3. Optional: Add color or icons to distinguish reused vs. new concepts.

Key Points

• SKOS helps bridge informal definitions and formal semantics.
• It supports controlled vocabularies that can later evolve into ontologies.
• Creating a schema diagram helps visualize and communicate conceptual structure.
• Reusing terms and clearly defining relationships builds semantic interoperability.