ontology-generator

# Ontology Generator for InfraNodus

Generate ontological knowledge graphs in InfraNodus format using [[wikilinks]] syntax. Output can be pasted directly into InfraNodus.com to visualize as a network and develop gaps and clusters with AI.

## Input Types

Accept two input types:

1. **Topic**: Generate comprehensive ontology for a given domain
2. **Text**: Extract ontological structure from provided text

## Entity Generation Principles

Generate comprehensive responses with multiple elements. Explore the full variety of entities belonging to the domain of inquiry. Include various types of:

- Entities
- Classes
- Relationships
- Axioms
- Rules

**Critical**: Avoid hierarchical structures with one central idea. First iteration should be comprehensive, long, and cover the widest possible domain. Generate network structures, not trees.

## Output Format

Each entity uses [[wikilink]] syntax. Relations are described in plain text within the same paragraph. Relation codes appear at paragraph end in [squarebrackets].

### Syntax Pattern

```
[[entity1]] relation description [[entity2]] [relationCode]
```

### Formatting Rules

- Each relation = separate paragraph line
- Minimum 8 paragraphs per relationship type
- Each statement MUST have at least 2 entities in [[wikilinks]]
- Each statement MUST have a [relationCode]

### Example

```
[[apple]] is an instance of [[fruit]] [isA]
[[apple]] grows as a result of [[apple blossom]] [causedBy]
[[apple]] has an oval [[shape]] [hasAttribute]
```

## Relation Codes

Use ONLY these relation codes (unless user provides alternatives):

- `[isA]` - Class membership
- `[partOf]` - Component relationship
- `[hasAttribute]` - Properties and characteristics
- `[relatedTo]` - General associations
- `[dependentOn]` - Dependencies
- `[causes]` - Causal relationships
- `[locatedIn]` - Spatial relationships
- `[occursAt]` - Temporal relationships
- `[derivedFrom]` - Origin and derivation
- `[opposes]` - Contradictory relationships

## Relationship Balance

Ensure relations cover both:

- **Descriptive aspects**: Classes, attributes, locations
- **Functional aspects**: Axioms, rules, causal chains

## Entity Distribution

- Avoid repeating the same entity excessively
- Focus on relations between entities
- Key entities may appear more frequently
- Result should resemble a network, not a tree

## Paragraph Structure Examples

### ❌ AVOID: Tree/Hierarchical Structure

This creates a hub-and-spoke pattern where one central entity dominates:

```
[[machine learning]] is a type of [[artificial intelligence]] [isA]
[[machine learning]] uses [[algorithms]] [relatedTo]
[[machine learning]] requires [[data]] [dependentOn]
[[machine learning]] produces [[predictions]] [causes]
[[machine learning]] has [[accuracy]] as a measure [hasAttribute]
[[machine learning]] is located in [[data science]] field [partOf]
[[machine learning]] occurs at [[training phase]] [occursAt]
[[machine learning]] is derived from [[statistics]] [derivedFrom]
```

**Problem**: "machine learning" appears in every statement, creating a star topology rather than a network.

### ✅ PREFERRED: Network Structure

Distribute entities across multiple interconnected relationships:

```
[[machine learning]] is a type of [[artificial intelligence]] [isA]
[[artificial intelligence]] enables [[automation]] of tasks [causes]
[[algorithms]] process [[training data]] to learn patterns [relatedTo]
[[training data]] must have high [[data quality]] [hasAttribute]
[[data quality]] affects [[model accuracy]] [causes]
[[model accuracy]] is measured during [[validation phase]] [occursAt]
[[validation phase]] comes after [[training phase]] [occursAt]
[[neural networks]] are derived from [[biological neurons]] [derivedFrom]
[[biological neurons]] are part of [[brain architecture]] [partOf]
[[supervised learning]] depends on [[labeled data]] [dependentOn]
[[labeled data]] opposes [[unlabeled data]] in requirements [opposes]
[[deep learning]] is a specialized form of [[neural networks]] [isA]
```

**Benefit**: Multiple entities interconnect, creating a web of relationships rather than radiating from one center.

### Example: Topic-Based Ontology (Climate Change)

Generate 8+ paragraphs per relationship type, distributed across entities:

```
[[climate change]] is caused by [[greenhouse gases]] [causes]
[[greenhouse gases]] include [[carbon dioxide]] as a component [partOf]
[[carbon dioxide]] has increasing [[atmospheric concentration]] [hasAttribute]
[[fossil fuels]] produce [[carbon dioxide]] when burned [causes]

[[global temperature]] is rising as an effect of [[climate change]] [causes]
[[ocean acidification]] is related to [[carbon dioxide]] absorption [relatedTo]
[[ice sheets]] are located in [[polar regions]] [locatedIn]
[[sea level rise]] depends on [[ice sheet melting]] [dependentOn]

[[renewable energy]] opposes [[fossil fuels]] as energy source [opposes]
[[solar power]] is a type of [[renewable energy]] [isA]
[[wind turbines]] generate [[electricity]] from wind [causes]
[[carbon capture]] is derived from [[industrial processes]] [derivedFrom]
```

### Example: Text-Based Extraction

When extracting from user-provided text, identify key entities and their explicit/implicit relationships:

**User text**: "Photosynthesis converts light energy into chemical energy. Chloroplasts contain chlorophyll which absorbs sunlight."

**Ontology output**:
```
[[photosynthesis]] converts [[light energy]] into forms [causes]
[[light energy]] becomes [[chemical energy]] through conversion [derivedFrom]
[[chloroplasts]] are located in [[plant cells]] [locatedIn]
[[chloroplasts]] contain [[chlorophyll]] as component [partOf]
[[chlorophyll]] has [[green color]] as property [hasAttribute]
[[chlorophyll]] absorbs [[sunlight]] for energy [relatedTo]
[[sunlight]] is a form of [[light energy]] [isA]
[[chemical energy]] is stored in [[glucose molecules]] [locatedIn]
```

### Balancing Relationship Types

Ensure each relation code appears 8+ time