From Terms to Meaning - Framing Knowledge with Competency Questions

Last updated on 2025-11-11 | Edit this page

Overview

Questions

  • What is a Competency Question (CQ) and how does it help in ontology development?

Objectives

  • Explain what a Competency Question (CQ) is and why it’s useful in ontology development.
  • Formulate domain-relevant CQs that reveal how concepts connect and what data relationships matter.
  • Use CQs to guide vocabulary refinement and early ontology design.
  • Understand how CQs validate whether a knowledge model meets its intended purpose.

Introduction

Why Competency Questions?

Think of CQs as the “user stories” of ontology design — they describe what users (researchers, managers, etc.) need to know or compare, and ensure your data terms and structures can support those needs.

They help you: - Focus on purpose-driven vocabulary development - Identify data gaps early - Build alignment between scientists, data managers, and modelers

Example: Salmon Data Integration Context

Imagine you have multiple datasets on sockeye salmon:

  • Fraser River dataset: smolt length, weight, and ocean entry date
  • Bristol Bay dataset: similar metrics, but uses different column names and sampling protocols

Possible Competency Questions might be:

  • “Is the average smolt condition at ocean entry higher in one population than another?”
  • “Do differences in smolt condition explain variation in adult return rates?”

From these questions, you can see what concepts need alignment: condition factor, smolt stage, population, region, and return abundance.

Discussion

Challenge 1: Identify decision points

Goal: Draft and refine CQs that reflect the research or management needs represented by your data.

Steps:

  1. Review your vocabulary terms or data dictionary from earlier modules.

  2. In small groups, brainstorm 3–5 natural-language questions that:

  • Are answerable using your data (or could be if integrated).
  • Require multiple terms or relationships to answer.
  • Reflect real research or management scenarios.

  1. Write each question on a sticky note or digital card.

  2. Group similar questions and discuss:

  • Which terms appear most often?
  • What relationships are implied?
Discussion

Challenge 2: Write your own competency questions

Goal: Identify which terms and relationships are needed to answer each CQ.

Instructions:

In small groups or pairs, write 2–3 CQs that your data integration or modeling efforts should be able to answer.

Focus on specific, realistic, and answerable questions — avoid vague ones like “What is salmon health?”

Check your questions:

  • Are key concepts clearly defined?
  • Do you know what data source could answer it?
  • What relationships would your ontology need to represent?

🧩 Example Revision:

Too broad: “What affects salmon survival?”

Better: “Does smolt condition at ocean entry affect adult return rates by region?”

Discussion

Challenge 3: Connect CQs to terms

Using your data dictionary from Modules 1–3:

  • Highlight which terms appear in your CQs.
  • Identify any missing terms or unclear definitions.
  • Note which terms might need alignment across datasets (e.g., “region,” “population,” “condition”).
Key Points
  • Competency Questions express the intended use of an ontology in natural language.
  • They help translate real-world research and management questions into conceptual structures.
  • CQs are iterative, evolving as you refine your vocabulary and build your ontology.
  • Good CQs are specific, testable, and connected to real data needs.