Bonus Session

Estimated time: 93 minutes

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.

Instructor Note

Facilitator Tips

• Keep the tone light and curious — emphasize exploration, not correctness.
  
• Encourage laughter when groups disagree — it mirrors real data harmonization.
  
• Use salmon-specific examples participants recognize to ground abstraction.
  
• End with a call to action: share a link or example ontology relevant to their domain (e.g., EnvO, Darwin Core, OBO Foundry).

Introduction

• Welcome participants and outline the workshop structure.
• Introduce the idea that ontologies = shared meaning frameworks — not just fancy dictionaries.
• Use a relatable example: “If one project measures ‘juvenile abundance’ and another measures ‘smolt density’, do we know if those data can be compared?”

Slide or flipchart talking points:

• Data integration challenges in salmon research:
  • Different terms for similar things.
  • Ambiguous definitions.
  • Data collected at different scales or life stages.
• How ontologies help:
  • Clarify relationships between terms.
  • Enable machine-readable data structures.
  • Support data discovery and reuse.

Transition:

“Let’s explore how our own thinking about salmon data reflects some of these challenges.”

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.

Instructor Note

Debrief Discussion (10 min)

Prompt questions:

• What patterns did you notice?
  
• Did your groups agree easily? Where did disagreements arise?
• Were any terms hard to place?
• What kinds of groupings emerged — by measurement type, by organism, by process?

Facilitator takeaway:

“This shows how even shared vocabulary can have different implicit structures — an ontology helps make those structures explicit.”

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?

Instructor Note

Miro key:

Spawner abundance | adult salmon population | abundance | census count at weir Capture efficiency | Sampling method event | error | statistical model, such as mark-recapture Ocean-age 3 | population demographic | count | age classification, but how??

Discussion (10 min)

• Which terms were most complex or ambiguous?
• How could decomposition help when mapping data between projects?
• Do you see examples where datasets might use the same word but mean different things?

Facilitator takeaway:

“Ontologies make these hidden structures explicit. When we define relationships clearly, our data become easier to integrate and reason over.”

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.

Instructor Note

Discussion

• How did your structure clarify meaning?
• What relationships felt intuitive vs forced?
• Where did alignment challenges appear when merging?

Facilitator takeaway:

“Ontologies don’t just name concepts — they define how those concepts relate. This structure is what lets systems automatically align datasets.”

Wrap-Up and Reflection (10 min)

Group discussion prompts:

• Where in your research workflows do you see ambiguity in data terms?
• Which tasks today reflected real challenges in your projects?
• What would be the first step towards adopting an ontology in your work?

Facilitator closing:

“By making meaning explicit, ontologies turn local data into shared, interoperable knowledge. Even small steps — like decomposing terms or defining relationships — move us toward more integrated salmon science.”

Optional Gamification Ideas

• Ontology Bingo: Each participant gets a bingo sheet with boxes like “Found a synonym”, “Identified hidden concept”, “Merged two groups”, “Defined an ‘is-a’ relationship”.
  Mark off during tasks; first to shout “Semantic alignment!” wins a small prize.

• Ontology Auction: Each group receives a “dataset” card (e.g., spawn timing dataset, tag detections, habitat surveys).
  They can “bid” for ontology terms (like event, location, organism, measurement) to integrate their data — illustrating the value of reusability.

Key Points

• Ontologies go beyond vocabulary—they structure meaning.

• Shared semantics make integration and reuse possible.

• Even small conceptual differences can block interoperability.