Metaverse Ontology - Hybrid Logseq + OWL Design

A formal ontology for metaverse concepts using an innovative hybrid approach that combines Logseq markdown for human readability with OWL Functional Syntax for formal reasoning.

🌟 Key Features

Orthogonal Classification: Two-dimensional design (Physicality × Role) enabling automatic inference of 9 intersection classes
Logseq-Native Format: Pure outline format with collapsed blocks, queryable properties, and section IDs
Automated Extraction: Rust tool to parse Logseq files and generate complete OWL ontology
OWL 2 DL Compliant: Full support for formal reasoning and consistency checking
Dual Purpose: Human-readable in Logseq, machine-extractable for reasoners
ETSI Aligned: Domain classification based on ETSI metaverse standards

📁 Project Structure

OntologyDesign/
├── README.md                          # This file
│
├── docs/                              # 📚 Documentation
│   ├── guides/
│   │   ├── QUICKSTART.md             # 5-minute setup guide
│   │   └── MIGRATION_GUIDE.md        # How to migrate concepts
│   ├── reference/
│   │   ├── TEMPLATE.md               # Standard concept format template
│   │   ├── FORMAT_STANDARDIZED.md    # Complete format specification
│   │   ├── URIMapping.md             # Wikilink → IRI conversion rules
│   │   └── LOGSEQ_TAG_USAGE.md       # Using metaverseOntology tag
│   ├── IMPLEMENTATION_STATUS.md      # Feature completion status
│   ├── MIGRATION_STATUS.md           # 260+ concept migration progress
│   └── task.md                       # Original design requirements
│
├── OntologyDefinition.md             # 🎯 Core ontology header & base classes
├── PropertySchema.md                  # 🔗 All object/data/annotation properties
├── ETSIDomainClassification.md       # 🏛️ ETSI domain taxonomy
├── ValidationTests.md                 # ✅ Test cases for reasoning
│
├── Avatar.md                          # 📘 Example: VirtualAgent class
├── DigitalTwin.md                     # 📗 Example: HybridObject class
│
├── VisioningLab/                      # 🔬 260+ concept files
│   ├── Game Engine.md                # Example: VirtualObject class
│   └── [...]                         # (migration in progress)
│
└── logseq-owl-extractor/             # 🦀 Rust extraction tool
    ├── Cargo.toml                     # Dependencies
    ├── README.md                      # Tool documentation
    └── src/
        ├── main.rs                    # CLI interface
        ├── parser.rs                  # Markdown parser
        ├── converter.rs               # Wikilink → IRI conversion
        └── assembler.rs               # Ontology assembly & validation

🚀 Quick Start

1. Build the Extractor

cd logseq-owl-extractor
cargo build --release

2. Extract OWL from Logseq Files

./logseq-owl-extractor/target/release/logseq-owl-extractor \
  --input . \
  --output metaverse-ontology.ofn \
  --validate

3. View in Protégé or Use with Reasoner

# Open in Protégé (GUI)
protege metaverse-ontology.ofn

# Or classify with whelk-rs (CLI)
whelk classify metaverse-ontology.ofn

📖 See docs/guides/QUICKSTART.md for detailed instructions.

🎯 Design Philosophy

Orthogonal Classification

The ontology uses two independent dimensions:

Physicality	Role	→ Result
Physical	Agent	PhysicalAgent (e.g., Human)
Virtual	Agent	VirtualAgent (e.g., Avatar)
Hybrid	Agent	HybridAgent (e.g., Cyborg)
Physical	Object	PhysicalObject (e.g., VR Headset)
Virtual	Object	VirtualObject (e.g., 3D Model)
Hybrid	Object	HybridObject (e.g., Digital Twin)
...	...	... (9 total combinations)

This allows:

Natural multiple inheritance
Automatic classification by reasoners
Clean separation of concerns

Logseq-Native Format

Each concept is defined in a pure Logseq outline format:

- ### OntologyBlock
  id:: avatar-ontology
  collapsed:: true
	- metaverseOntology:: true
	- term-id:: 20067
	- preferred-term:: Avatar
	- definition:: Digital representation of a person...
	- owl:class:: mv:Avatar
	- owl:physicality:: VirtualEntity
	- owl:role:: Agent
	- owl:inferred-class:: mv:VirtualAgent
	- #### Relationships
	  id:: avatar-relationships
		- has-part:: [[Visual Mesh]], [[Animation Rig]]
		- requires:: [[3D Rendering Engine]]
	- #### OWL Axioms
	  id:: avatar-owl-axioms
	  collapsed:: true
		- ```clojure
		  Declaration(Class(mv:Avatar))
		  SubClassOf(mv:Avatar mv:VirtualEntity)
		  SubClassOf(mv:Avatar mv:Agent)
		  ```
- ## About Avatars
	- Human-readable description and examples...

Benefits:

✅ Tidy: Everything collapses into ### OntologyBlock
✅ Queryable: metaverseOntology:: true tag enables Logseq queries
✅ Referenceable: Section IDs allow block references
✅ Readable: Clojure syntax highlighting for OWL code
✅ Extractable: Parser extracts properties and OWL blocks
✅ Documented: Human context in "About" section
✅ Linked: WikiLinks create knowledge graph

📚 Documentation

Getting Started

Document	Description
QUICKSTART.md	Get started in 5 minutes
MIGRATION_GUIDE.md	How to migrate VisioningLab concepts

Reference

Document	Description
TEMPLATE.md	Standard concept format template
FORMAT_STANDARDIZED.md	Complete format specification
URIMapping.md	Wikilink to IRI conversion rules
LOGSEQ_TAG_USAGE.md	Using metaverseOntology tag for queries

Additional Resources

Document	Description
CONSOLIDATED_KNOWLEDGEBASE.md	Complete project overview
FORWARD_IMPLEMENTATION_PLAN.md	Future enhancements roadmap
SOLUTION_ARCHITECTURE_STRATEGY.md	Technical architecture details

Tools

Document	Description
logseq-owl-extractor/README.md	Extractor tool documentation

🧪 Example Concepts

Avatar.md - VirtualAgent

Classification:

owl:physicality:: VirtualEntity
owl:role:: Agent
owl:inferred-class:: mv:VirtualAgent ✅

Key Properties:

Represents exactly one user or AI agent
Requires 3D Rendering Engine
Enables User Embodiment and Social Presence
Has parts: Visual Mesh, Animation Rig

OWL Axioms:

Declaration(Class(mv:Avatar))
SubClassOf(mv:Avatar mv:VirtualEntity)
SubClassOf(mv:Avatar mv:Agent)
SubClassOf(mv:Avatar
  ObjectExactCardinality(1 mv:represents mv:Agent)
)

➡️ Reasoner infers: Avatar ⊑ VirtualAgent

DigitalTwin.md - HybridObject

Classification:

owl:physicality:: HybridEntity
owl:role:: Object
owl:inferred-class:: mv:HybridObject ✅

Key Properties:

Binds physical entity to virtual representation
Synchronizes data in real-time
Requires IoT sensors and data streams
Implemented in Infrastructure Layer

OWL Axioms:

Declaration(Class(mv:DigitalTwin))
SubClassOf(mv:DigitalTwin mv:HybridEntity)
SubClassOf(mv:DigitalTwin mv:Object)
SubClassOf(mv:DigitalTwin
  ObjectExactCardinality(1 mv:synchronizesWith mv:PhysicalEntity)
)

➡️ Reasoner infers: DigitalTwin ⊑ HybridObject

VisioningLab/Game Engine.md - VirtualObject

Classification:

owl:physicality:: VirtualEntity
owl:role:: Object
owl:inferred-class:: mv:VirtualObject ✅

Key Properties:

Software platform for real-time 3D experiences
Has parts: Rendering Pipeline, Physics Engine, Asset Manager
Belongs to InfrastructureDomain and CreativeMediaDomain

➡️ Reasoner infers: GameEngine ⊑ VirtualObject

✅ Validation Tests

The ontology includes test cases for:

✅ Inference: Avatar → VirtualAgent classification
✅ Consistency: Valid Digital Twin with proper bindings
⚠️ Inconsistency Detection: Digital Twin missing required binding
❌ Disjointness Violation: Entity in both Physical and Virtual

See ValidationTests.md for details.

💡 Use Cases

1. Metaverse Interoperability Standards

Define common vocabulary for cross-platform metaverse systems
Enable semantic interoperability between virtual worlds
Support ETSI and ISO metaverse standardization efforts

2. Knowledge Graph Construction

Build queryable knowledge base of metaverse concepts
Link related concepts through formal relationships
Support semantic search and discovery

3. Application Development

Reference ontology for metaverse platform architects
Semantic validation of system designs
Documentation and communication tool for teams

4. Research & Analysis

Classify emerging metaverse technologies
Track evolution of metaverse concepts over time
Identify gaps and opportunities in the metaverse stack

5. Automated Reasoning

Automatically infer concept classifications
Detect inconsistencies in system designs
Validate architectural constraints

🔧 Technology Stack

Component	Technology	Purpose
Ontology Language	OWL 2 DL	Formal knowledge representation
Syntax	OWL Functional Syntax	Machine-readable axioms
Source Format	Logseq Markdown	Human-readable editing
Parser/Validator	horned-owl (Rust)	OWL parsing and validation
Reasoner	HermiT, Pellet, whelk-rs	Automatic classification and consistency checking
Knowledge Editor	Logseq	Visual outliner for navigation
Ontology Viewer	Protégé	GUI for visualizing class hierarchy
Extraction Tool	Custom Rust tool	Convert Logseq → OWL
Version Control	Git + Markdown	Track changes over time

🛠️ Extractor Tool Features

The logseq-owl-extractor Rust tool provides:

📝 Parse Logseq markdown - Extracts properties and OWL blocks from outline format
🔗 Combine OWL blocks - Assembles axioms from multiple concept files
🏗️ Build complete ontology - Includes header from OntologyDefinition.md
✅ Validate syntax - Uses horned-owl to check OWL 2 DL compliance
🔄 Convert wikilinks - Transforms [[Page Name]] to mv:PageName IRIs
🎯 Property conversion - Optional conversion of Logseq properties to OWL axioms
📊 Two format support - Handles both code fence and direct indented OWL blocks
🔍 Error reporting - Clear messages for syntax errors with line numbers

Example usage:

logseq-owl-extractor --input . --output ontology.ofn --validate

See logseq-owl-extractor/README.md for full documentation.

📊 Current Status

Production Ready - Full 281-concept metaverse ontology with OWL 2 DL compliance

Component	Status
Metaverse Concepts	✅ 281 concepts fully migrated
OWL Validation	✅ Zero errors, OWL 2 DL compliant
Multi-format Export	✅ OWL/XML, Turtle, JSON-LD
Visualization	✅ WebVOWL ready
Documentation	✅ Complete

🤝 Contributing

Contributions welcome! To add a new concept:

Use the template: Copy docs/reference/TEMPLATE.md
Follow exemplars: Study Avatar.md, DigitalTwin.md, or VisioningLab/Game Engine.md
Classify correctly: Use Physicality × Role dimensions (see docs/guides/MIGRATION_GUIDE.md)
Add metaverseOntology tag: Include metaverseOntology:: true property
Validate: Run the extractor to check OWL syntax
Submit PR: Include description of concept and its classification

Adding New Concepts - Quick Checklist

See docs/reference/TEMPLATE.md for full validation checklist.

📖 Learn More

OWL 2 Resources

Metaverse Standards

Tools

Protégé - Ontology editor
whelk-rs - Fast OWL 2 EL reasoner in Rust
ROBOT - OWL tool for command line

📄 Licence

This project is licensed under the Mozilla Public Licence 2.0. See the LICENSE file for details.

👥 Authors

John O'Hare for DreamLab - AI Ltd based on VisioningLab open Metaverse Ontology

🙏 Acknowledgments

Built with horned-owl by Phillip Lord
Inspired by the Logseq knowledge management approach
Aligned with ETSI and ISO metaverse standards

🎉 Ready to Explore?

Visualize the Ontology:

Download metaverse-ontology-webvowl.owl
Visit WebVOWL
Upload and explore 281+ metaverse concepts interactively!

Additional Documentation:

Complete Knowledge Base - Detailed project overview
Implementation Roadmap - Future enhancements
Architecture Details - Technical design

Name		Name	Last commit message	Last commit date
Latest commit History 50 Commits
.claude		.claude
VisioningLab		VisioningLab
docs		docs
logseq-owl-extractor		logseq-owl-extractor
.gitignore		.gitignore
Avatar.md		Avatar.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
DigitalTwin.md		DigitalTwin.md
ETSIDomainClassification.md		ETSIDomainClassification.md
OntologyDefinition.md		OntologyDefinition.md
PropertySchema.md		PropertySchema.md
README.md		README.md
convert_owl_to_ttl.py		convert_owl_to_ttl.py

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Metaverse Ontology - Hybrid Logseq + OWL Design

🌟 Key Features

📁 Project Structure

🚀 Quick Start

1. Build the Extractor

2. Extract OWL from Logseq Files

3. View in Protégé or Use with Reasoner

🎯 Design Philosophy

Orthogonal Classification

Logseq-Native Format

📚 Documentation

Getting Started

Reference

Additional Resources

Tools

🧪 Example Concepts

Avatar.md - VirtualAgent

DigitalTwin.md - HybridObject

VisioningLab/Game Engine.md - VirtualObject

✅ Validation Tests

💡 Use Cases

1. Metaverse Interoperability Standards

2. Knowledge Graph Construction

3. Application Development

4. Research & Analysis

5. Automated Reasoning

🔧 Technology Stack

🛠️ Extractor Tool Features

📊 Current Status

🤝 Contributing

Adding New Concepts - Quick Checklist

📖 Learn More

OWL 2 Resources

Metaverse Standards

Tools

📄 Licence

👥 Authors

🙏 Acknowledgments

🎉 Ready to Explore?

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