Metaverse Ontology

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: A two-dimensional design (Physicality × Role) enables the automatic inference of 9 intersection classes.
• Logseq-Native Format: Concepts are defined in a pure outline format with properties, queryable tags, and collapsible blocks, making them easy to read and manage in Logseq.
• Automated Extraction: A Rust-based tool parses the Logseq markdown files and generates a complete, valid OWL ontology.
• OWL 2 DL Compliant: The generated ontology fully supports formal reasoning and consistency checking with standard OWL 2 DL tools.
• ETSI Aligned: The domain classification is based on the European Telecommunications Standards Institute (ETSI) metaverse standards.

🎨 Visualization

The ontology can be explored visually using modern web-based tools that support the Turtle (.ttl) format.

Recommended Visualizers

• OWL TTL Web Visualizer: An interactive 3D force-directed graph visualizer that runs in your web browser. This tool uses Neo4j to render the ontology graph, allowing you to explore nodes and their connections dynamically. See the Visualizer Guide for setup and usage instructions.
• VisionFlow System: This ontology module is a component of the broader VisionFlow Knowledge Management System, which provides advanced visualization and knowledge exploration capabilities.

To use these visualizers, the ontology must first be converted from its native OWL Functional Syntax (.ofn) to the Turtle (.ttl) format.

📁 Project Structure

Metaverse-Ontology/
├── README.md                          # This file
│
├── docs/                              # 📚 Documentation
│   ├── guides/
│   │   ├── QUICKSTART.md             # 5-minute setup guide
│   │   └── USER_GUIDE.md             # Guide to using the ontology
│   ├── reference/
│   │   ├── TEMPLATE.md               # Template for new concepts
│   │   └── URIMapping.md             # Rules for converting Wikilinks to IRIs
│   └── visualizer-guide.md           # Guide for the OWL TTL Web Visualizer
│
├── OntologyDefinition.md             # 🎯 Core ontology header & base classes
├── PropertySchema.md                  # 🔗 All defined properties
├── ETSIDomainClassification.md       # 🏛️ ETSI domain taxonomy
│
├── Avatar.md                          # 📘 Example: VirtualAgent class
├── DigitalTwin.md                     # 📗 Example: HybridObject class
│
├── *.md                              # Over 280 other concept files
│
├── logseq-owl-extractor/             # 🦀 Rust tool for OWL extraction
│
└── convert_owl_to_ttl.py             # 🐍 Python script for TTL conversion

🚀 Quick Start

Prerequisites

• Rust and Cargo: Install from rustup.rs.
• Python 3: For the conversion script.
• ROBOT (optional but recommended): A command-line tool for ontology tasks. Download it from http://robot.obolibrary.org/.

1. Build the Extractor

cd logseq-owl-extractor
cargo build --release
cd ..

2. Extract the Ontology (.ofn)

This command parses all markdown files and generates a single ontology file in OWL Functional Syntax.

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

3. Convert to Turtle (.ttl) for Visualization

Use a tool like ROBOT to convert the .ofn output to .ttl.

robot convert --input metaverse-ontology.ofn --output metaverse-ontology.ttl

Note: The included convert_owl_to_ttl.py is a basic script; ROBOT is recommended for more robust conversions.

4. Visualize the Ontology

Follow the instructions in the Visualizer Guide to load your metaverse-ontology.ttl file into the OWL TTL Web Visualizer.

🎯 Design Philosophy

Orthogonal Classification

The ontology uses two independent dimensions to classify every concept:

Physicality	Role	→ Inferred Class	Example
Physical	Object	PhysicalObject	VR Headset
Virtual	Agent	VirtualAgent	Avatar
Hybrid	Object	HybridObject	Digital Twin
(...and 6 other combinations)

This design allows for powerful automated classification and ensures a clean separation of concerns.

Logseq-Native Format

Each concept is defined in a human-readable Logseq outline, which is then parsed by the extractor.

- ### 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
		- has-part:: [[Visual Mesh]], [[Animation Rig]]
	- #### 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 of this approach:

• Human-Readable: Easy to browse and edit in Logseq.
• Machine-Readable: The Rust tool reliably extracts formal axioms.
• Linked Data: [[Wikilinks]] create a connected knowledge graph within Logseq.
• Documented: Formal definitions live alongside rich, human-readable context.

🔧 Technology Stack

Component	Technology	Purpose
Ontology Language	OWL 2 DL	Formal knowledge representation
Source Format	Logseq Markdown	Human-readable editing and documentation
Extraction Tool	Custom Rust Tool	Converts Logseq markdown to OWL
Parser/Validator	horned-owl (Rust)	OWL parsing and syntax validation
Visualization	OWL TTL Web Visualizer	Interactive 3D graph exploration
Version Control	Git	Tracks changes to the ontology source

🤝 Contributing

Contributions are highly encouraged! To add or improve a concept:

1. Read the guidelines: See the full CONTRIBUTING.md file.
2. Use the template: Copy docs/reference/TEMPLATE.md.
3. Classify correctly: Apply the Physicality × Role dimensions.
4. Add the tag: Ensure metaverseOntology:: true is present.
5. Validate your changes: Run the extractor tool to check for syntax errors.
6. Submit a Pull Request.

📄 License

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