Bonus Session

Last updated on 2025-11-14 | Edit this page

Ontology Game Workshop

Making Sense of Salmon Research Data

Overview

Questions

  • What is an ontology, and how does it differ from a data dictionary?
  • Why does salmon research data need clearer semantics?
  • What challenges arise when different people organize the same vocabulary?

Objectives

  • Define “ontology” in the context of data integration.
  • Recognize data ambiguity problems common in salmon science.
  • Connect these problems to ontology-based solutions.
  • Recognize why implicit structures must become explicit in an ontology.
  • Reflect on the need for controlled vocabularies.

Introduction

An ontology acts as the database schema or rule book for your knowledge model. It defines:

Types of entities (nodes): “Person,” “Film,” “Genre.”

Types of relationships (edges): A “Person” can “direct” or “star in” a “Film.”

Properties for entities and relationships: A “Person” has a “name” and “birth year.”

Ontologies provide foundational rules and structure, ensuring consistent interpretation by both humans and machines.

Understanding transitive properties

A transitive property is a relationship where if A relates to B, and B relates to C, then A relates to C.

Here is an example of how different life stages of salmon relate to spawning events, using a few clear classes and one transitive property.

We often record salmon data at different points in their life cycle — for example, a smolt Migration Event one year and a Spawning Event several years later.

By using a transitive property like hasLifeStageEvent, we can reason that the same Stock is connected across all those events.

Transitive properties help us:

  • Represent hierarchies (e.g., Species → Population → Individual).

  • Capture temporal or process chains (e.g., Smolt → Adult → Spawner).

  • Enable reasoning that connects related concepts without manually writing every link.

Callout 
Stock_A hasLifeStageEvent SmoltMigration_2022 .

SmoltMigration_2022 hasLifeStageEvent Spawning_2025 .

thus

Stock_A hasLifeStageEvent Spawning_2025 .
Discussion

Sorting the Vocabulary Soup (20 min)

Goal: Experience how people intuitively categorize domain concepts — and how different those categories can be.

Materials

  • Card sets with one term per card (20–30 total per table)
  • Example terms:
    water temperature, age, length, weight, life stage, spawn date, smolt, tag ID, river reach, capture event, habitat type, species, sex, growth rate, migration timing
  • Timer (10–15 minutes)
  • Table space or wall for grouping

Instructions

  1. Each group gets a shuffled deck of term cards.
  2. Ask them to organize terms into groups that make sense to them.
    No rules — they can group by theme, data type, biological scale, etc.
  3. Once grouped, have each group name their categories.
  4. Optional: groups walk around and view each others’ arrangements.

Implicit meanings of compound terms

We often see compound terms within dataset columns, yet compound terms often embed multiple concepts. For example, “Mark-recapture escapement estimate” includes:

  • Entity: population
  • characteristic: escapement
  • Measurement Method: mark-recapture

We cannot express these implicit meanings inside the table without first decomposing the term. Understanding these components helps clarify meaning and supports data integration.

Discussion

Dissecting Salmon Terms (20 min)

Goal: Reveal the hidden components and embedded meanings in compound terms.

Materials

  • Each group receives 4–6 “compound” term cards.
    Examples:
    • Life stage
    • Spawner abundance
    • Tag detection event
    • Smolt-to-adult return rate (SAR)
    • Migration success
    • Egg-to-fry survival
  • Blank mini-cards or sticky notes
  • Pens or markers
  • Labels for concepts: Entity, Property, Process, Event, Assertion, etc.

Instructions

  1. Each group breaks down each compound term into its atomic concepts.
    • Example:
      Life stage → organism + developmental phase + (implied) habitat + age range
      Tag detection event → tagged individual + receiver + location + time
  2. Write each sub-concept on separate sticky notes.
  3. Label the type of each component (e.g. property, process).
  4. Optional challenge: Which team can identify the most distinct sub-concepts in 5 minutes?
Discussion

Build a Mini-Ontology (20 min)

Goal: Experience how explicit relationships can organize knowledge.

Materials

  • Blank cards or sticky notes (reuse from previous task)
  • Printed relationship arrows or connectors labeled:
    • is a
    • has property
    • occurs at
    • involves
    • measured in
    • related to
  • Optional: string or tape to connect items on a wall or table

Instructions

  1. Using the decomposed concepts from Task 2, groups now connect them into a network using relationship arrows.
    • Example:
      Tag detection event involves tagged individual
      Tag detection event occurs at location
      location has property river reach
  2. Encouraged to build small hierarchies (e.g., smolt is a juvenile is a life stage).
  3. Optional: introduce a “data integration twist” — merge two groups’ mini-ontologies and reconcile overlapping terms.
Key Points

  • Ontologies go beyond vocabulary—they structure meaning.

  • Shared semantics make integration and reuse possible.

  • Even small conceptual differences can block interoperability.