Module 5. Designing Your Own Humanities Data Model

Course 3. Modeling Meaning: How We Structure Humanities Data
Estimated Time: 40+ minutes

🧭 Module Objectives

Outline a conceptual data model for a humanities topic or collection.
Identify appropriate entities, relationships, and attributes.
Define key contextual and interpretive dimensions for your model.
Translate your conceptual model into a visual diagram or schema.
Reflect critically on how your design choices express meaning and perspective.

From Concept to Model

In previous modules, you learned how data models represent meaning:

Module 1 defined modeling as a translation between the world and data.
Module 2 introduced the building blocks of structure.
Module 3 compared ways of representing knowledge.
Module 4 explored context, ambiguity, and ethics.

Now, you'll bring these ideas together, designing a small but thoughtful data model of your own!

Step One: Choose Your Focus

Select a humanities subject you care about. It might be:

An artist (e.g., Taylor Swift, Nina Simone, Van Gogh, Picasso)
A text (a novel, poem, or collection)
A place (a city, heritage site, or digital archive)
An event (e.g., the US Civil War, bombing of Pearl Harbor, last week's mac-and-cheese dinner)
A theme (migration, protest, love, identity, technology)

Ask yourself: "What aspects of this subject do I want to understand, compare, or reveal?" This guiding question will shape what your model needs to represent.

Step Two: Identify the Core Entities

List 3–6 key entities (the "nouns") your model will include. For example, in a Wellespring context:

Entity	Description
Song	A musical work written or performed by an artist
Person	A songwriter, performer, collaborator, or any other human
Theme	A recurring idea expressed through lyrics
Event	Any actual event, including live performances
Reaction	A listener response, review, or social media post

Each entity type should represent a category of meaning relevant to your project: something you might want to connect, analyze, or visualize later.

Step Three: Define Relationships

Next, describe how these entities connect—this is where you add in your verbs.

Relationship	From → To	Meaning
WROTE_SONG	Song → Person	Identifies a song's writer
EXPRESSES	Song → Theme	Indicates that a song embodies a theme
PERFORMED_DURING	Song → Event	Links a song to a specific performance
MENTIONED_IN	Song → Reaction	Shows audience engagement or discourse

Keep relationships directional and meaningful. Ask yourself: "What do I want these connections to say about the cultural world I'm studying?"

Step Four: Add Attributes

Each entity and relationship can have attributes: descriptive details.

Entity	Example Attributes
Song	title, releaseDate, length, mood
Person	name, birthYear, role, location
Theme	name, category, sentiment
Event	date, place, audienceSize
Reaction	platform, tone, keywords

Attributes let you filter and compare, e.g., "show all songs with positive sentiment performed at Farm Aid."

Step Five: Sketch the Model

Use a pencil and paper, a whiteboard, or a simple digital diagramming tool (e.g., arrows.app, draw.io, Lucidchart, or even PowerPoint). Start with nodes (circles) for entities, draw arrows for relationships, and label them clearly.

Add a few key attributes under each node to show what distinguishes each instance. This diagram is your conceptual schema: the blueprint for later database design.

Step Six: Reflect on Interpretation and Ethics

Every modeling decision carries interpretive weight:

Why did you include some entities and not others?
What does your choice of relationships suggest about causality or influence?
How do you represent ambiguity, or avoid claiming more than you know?

Document your reasoning in a brief reflective paragraph. This is the humanist's equivalent of metadata for your model: transparency about perspective and purpose.

If you're completing this course with peers or classmates, exchange models and discuss:

What patterns or insights does each reveal?
What questions or blind spots emerge?
How might your models complement or contradict each other?

In the Wellespring Project, this dialogic process mirrors how knowledge graphs evolve collaboratively: different voices contributing to a richer picture of cultural meaning.

Looking Ahead: From Model to Graph

In our next course, Building Graphs in Neo4j, you’ll learn to:

Implement your model in a real graph database.
Create and query nodes, relationships, and properties.
Visualize your network to uncover patterns and stories.

Your conceptual model here will serve as the foundation for that technical work. Modeling meaning is the art of thinking relationally; graphing meaning is the act of building it interactively.

Key Takeaways

A good humanities data model begins with clear purpose and interpretive transparency.
Entities, relationships, and attributes should emerge from meaningful questions.
Visual modeling helps reveal structure, gaps, and potential insights.
Reflection ensures ethical awareness and contextual depth.
Your model is both a research tool and a scholarly argument.

Course 3. Modeling Meaning: How we structure humanities data

Module 5. Designing Your Own Humanities Data Model

🧭 Module Objectives

From Concept to Model

Step One: Choose Your Focus

Step Two: Identify the Core Entities

Step Three: Define Relationships

Step Four: Add Attributes

Step Five: Sketch the Model

Step Six: Reflect on Interpretation and Ethics

Looking Ahead: From Model to Graph

Key Takeaways

Knowledge Check & Reflection

Suggested Readings & Resources

Course 3. Modeling Meaning: How we structure humanities data

🧭 Module Objectives

From Concept to Model

Step One: Choose Your Focus

Step Two: Identify the Core Entities

Step Three: Define Relationships

Step Four: Add Attributes

Step Five: Sketch the Model

Step Six: Reflect on Interpretation and Ethics

Step Seven: Share and Compare

Looking Ahead: From Model to Graph

Key Takeaways

Knowledge Check & Reflection

Suggested Readings & Resources