Executable ontology bridging cultural meaning and computational reproducibility.
🔮 From cultural symbols to reproducible computation — bridging meaning with code.
This repository contains the complete source code, data, and validation harnesses for the research paper, "Resonant Structures of Meaning: A Machine-Executable Ontology for Interpretive AI."
Abstract: This paper introduces Resonant Structures of Meaning (RSM), a machine-executable ontology for symbolic and interpretive AI. Unlike conventional pipelines that begin with theoretical frameworks and only later attempt code translation, RSM adopts a code-first methodology: executable systems serve as primary research artifacts from which theoretical insights and methodological structures are derived.
👉 Live Demo on Hugging Face Spaces
Run RSM in your browser — no installation required!
- ⚡ VME (Vector of Meaning Energy) – Encodes cultural symbols into normalized, machine-readable vectors.
- 🎼 RI (Resonance Index) – Quantifies interpretive alignment and harmony between symbols.
- 🛰️ DriftSentinel – Monitors the stability of symbolic meanings over time.
- ⚖️ LawBinder – Provides a framework for resolving conflicts between different symbolic systems.
Click to expand: The 4 Core Mechanisms
-
VME (Vector of Meaning Energy)
- What it is: A normalized vector encoding that represents symbolic data from diverse cultural systems (e.g., Tarot, Saju, Astrology) in a unified mathematical space.
- Its function: Translates abstract cultural symbols into concrete, machine-readable data.
-
RI (Resonance Index)
- What it is: A quantitative metric (0.0 to 1.0) that measures the degree of interpretive alignment and harmony between a set of symbolic vectors.
- Its function: Provides an objective score for how well different symbols "work together."
-
DriftSentinel
- What it is: A monitoring module that tracks the stability of the Resonance Index (RI) over time or across different contexts.
- Its function: Detects and alerts on "interpretive drift," ensuring that meanings remain consistent and reproducible.
-
LawBinder
- What it is: A conflict-resolution framework that applies explicit strategies (e.g., harmonize, prioritize) when aggregating vectors from multiple ontologies.
- Its function: Manages disagreement and maintains coherence when combining different systems of meaning.
Click to expand: Project Philosophy & Structure
Philosophy: RSM addresses fundamental reproducibility challenges in interpretive AI by grounding symbolic reasoning in executable, auditable artifacts. By treating code as the primary ontology, it inverts the traditional "theory-first" pipeline, thereby minimizing the gap between conceptual design and computational implementation.
File & Directory Guide:
.
├── engine/ # Core computational engines (VME, RI, DriftSentinel)
├── data/ # Symbolic databases in JSON format
├── tests/ # The full validation test suite (>100 tests)
├── paper/ # The academic research paper (PDF)
├── docs/ # Documentation and images
│ └── images/
├── huggingface_deployment/ # Self-contained code for the HF Spaces demo
├── rsm_simulator.py # Main interactive simulator (local execution)
└── README.md # You are here!
-
Clone the repository:
git clone https://github.com/Flamehaven/rsm-ontology.git cd rsm-ontology -
Install dependencies:
pip install -r requirements.txt
-
Run the Simulator & Validation Suite:
# Change directory to the project folder first cd "Resonant Structures of Meaning A Machine-Executable Ontology for Interpretive AI" # Run the simulator python rsm_simulator.py
- Extend symbolic systems (e.g., I Ching, Runes)
- Implement Reinforcement Learning-based
LawBinder - Develop automated calibration pipelines
- Add more visualization outputs
Have questions or ideas? Join the conversation!
Contributions are welcome! Please feel free to submit a Pull Request or open an Issue.
If you use this framework in your research, please cite the paper:
@article{kwansub2025rsm,
title={Resonant Structures of Meaning: A Machine-Executable Ontology for Interpretive AI},
author={Yun, Kwansub},
journal={Flamehaven Initiative},
year={2025},
note={https://github.com/Flamehaven/rsm-ontology}
}
🌟 Star this repo if you find it useful! 🌟
🤝 Contribute / 🔗 Follow Project
This project is licensed under the MIT License.