Create a ConceptMap diagram as PNG image. A Concept Map defines the vocabulary of a domain through falsifiable propositions. It helps people align on exact wordings and shared understanding by stating facts as simple, readable sentences.

You provide VGL (Vithanco Graph Language) code using the ConceptMap notation and the tool renders it to an PNG image.

How to Build a Concept Map

1. Start with a Guiding Question (used as the graph title) — it determines what belongs on the map.

2. List all relevant concepts.

3. Connect every concept through relations. ALWAYS form readable propositions.

4. NEVER leave a concept unconnected. Every concept MUST connect to at least one relation.

5. Reuse relations when multiple concepts share the same relationship.

VGL Syntax

vgraph <id>: ConceptMap "<Guiding Question>" {
    <nodes and edges>
}

Node Types

• Concept — a concept or term

• EmphasizedConcept — a concept to highlight as especially important

• Relation — a linking verb or phrase that connects concepts

node <id>: Concept "<label>"
node <id>: EmphasizedConcept "<label>"
node <id>: Relation "<label>"

Edges

edge <from_id> -> <to_id>

ALWAYS form the pattern: Concept -> Relation -> Concept. This creates a readable proposition.

Reusing Relations

When multiple concepts share the same relationship, reuse a single Relation node. No duplicate edges when reusing — create edges only where needed:

Multiple sources, one target:

vgraph animals: ConceptMap "What are common pets?" {
    node dog: Concept "Dog"
    node cat: Concept "Cat"
    node isa: Relation "is a"
    node mammal: Concept "Mammal"

    edge dog -> isa
    edge cat -> isa
    edge isa -> mammal  // Only ONE edge from relation to target
}

Both "Dog is a Mammal" and "Cat is a Mammal" share one Relation node — only 4 nodes total.

One source, multiple targets:

vgraph typography: ConceptMap "What defines a font?" {
    node font: Concept "Font"
    node has: Relation "has"
    node weight: Concept "Weight"
    node style: Concept "Style"

    edge font -> has  // Only ONE edge from source to relation
    edge has -> weight
    edge has -> style
}

Both "Font has Weight" and "Font has Style" share one Relation node — only 4 nodes total.

CRITICAL — Multiple inbound AND multiple outbound edges:

When a relation has BOTH multiple inbound edges (concepts pointing TO the relation) AND multiple outbound edges (relation pointing TO concepts), ALL inbound concepts must make sense as propositions with ALL outbound concepts. With n inbound and m outbound edges, you get n × m propositions — all must be valid.

Invalid example:

vgraph learning: ConceptMap "What enables growth?" {
    node learning: Concept "Learning"
    node accountability: Concept "Accountability"
    node enables: Relation "enables"

    edge learning -> enables
    edge accountability -> enables
    edge enables -> learning
    edge enables -> accountability
}

This creates 4 propositions (2 × 2):

• Learning enables Learning ❌ (circular)

• Learning enables Accountability ✓

• Accountability enables Learning ✓

• Accountability enables Accountability ❌ (circular)

Fix — Use specific relations:

vgraph learning: ConceptMap "What enables growth?" {
    node learning: Concept "Learning"
    node accountability: Concept "Accountability"
    node facilitates: Relation "facilitates"
    node requires: Relation "requires"

    edge accountability -> facilitates
    edge facilitates -> learning

    edge learning -> requires
    edge requires -> accountability
}

Now: "Accountability facilitates Learning" ✓ and "Learning requires Accountability" ✓

Fix — Restructure:

vgraph learning: ConceptMap "What enables growth?" {
    node learning: Concept "Learning"
    node accountability: Concept "Accountability"
    node environment: Concept "Environment"
    node creates: Relation "creates"

    edge learning -> creates
    edge accountability -> creates
    edge creates -> environment
}

Now: "Learning creates Environment" ✓ and "Accountability creates Environment" ✓

Complete Example

vgraph learningCM: ConceptMap "What is Learning?" {
    node student: Concept "Student"
    node subject: Concept "Subject"
    node practice: EmphasizedConcept "Practice"
    node understanding: Concept "Understanding"
    node resources: Concept "Resources"

    node learns: Relation "learns"
    node requires: Relation "requires"
    node leads_to: Relation "leads to"
    node uses: Relation "uses"

    edge student -> learns
    edge learns -> subject
    edge subject -> requires
    edge requires -> practice
    edge practice -> leads_to
    edge leads_to -> understanding
    edge subject -> uses
    edge uses -> resources
}

Propositions: Student learns Subject, Subject requires Practice, Practice leads to Understanding, Subject uses Resources.

Rules

1. ALWAYS form readable propositions — every Concept -> Relation -> Concept chain MUST read as a natural, falsifiable sentence

2. NEVER leave a concept unconnected

3. Concept labels are nouns — concept labels should be nouns or noun phrases (things, ideas, entities), not actions, sentences, or verb phrases. Use "Understanding" not "How we understand things"

4. Verb agreement — Relation labels must match the subject's number: singular concepts (e.g., "Student", "Dog") use singular verbs ("requires", "is", "has"); plural concepts (e.g., "LLMs", "Dogs") use plural verbs ("require", "are", "have"). For mixed cases, use infinitive form without "(s)": "enable" not "enables", "prevent" not "prevents". The notation "Dog(s)" in concept labels can indicate the concept works in both forms, then use infinitive verb forms.

5. Guiding question as filter — the guiding question determines what belongs on the map. Every concept MUST help answer it. If a concept does not contribute to answering the guiding question, it does not belong.

6. Relationship diversity — use diverse relation types (not all "is a" or "has"). Avoid simple opposite pairs (e.g. "is parent of" / "is child of" are the same relationship stated twice in different directions). Use different verbs that reveal distinct aspects of how concepts relate.

7. Rich connectivity — every concept should connect to at least 2 others via different relations. Aim for a network structure where multiple paths exist between concepts — not a chain (linear A→B→C) or a spoke (one central concept with everything hanging off it).

8. Use meaningful IDs (student, practice — not n1, n2)

9. Keep relation labels short (verbs or short phrases)

10. Use EmphasizedConcept sparingly

11. Introduce abbreviations — if a concept label uses an abbreviation, spell out the full term first: e.g. "Gross National Product (GNP)", not just "GNP"