Permanent Data Encoding (PDE): A Visual Language for Semantic Compression and Knowledge Preservation in 3-Character Units

2025-07-27

AI Summary

To address challenges in long-term knowledge preservation—namely, overreliance on digital systems, offline inaccessibility, and intergenerational unintelligibility—this paper proposes a non-electric, human-readable visual language framework. The method employs 2–3-character glyphs as atomic semantic units, integrated with a public dictionary protocol and rule-based semantic expansion, enabling high-density semantic compression and transparent, self-contained visual parsing. Its core contribution lies in unifying lightweight encoding with logically derivable syntax, thereby supporting persistent, maintenance-free storage, manual decoding, and logical reconstruction without power. Experimental evaluation demonstrates robustness and interpretability in disaster recovery and human-AI collaborative scenarios. The framework establishes a deployable, zero-maintenance semantic substrate for intergenerational knowledge infrastructure.

Technology Category

• Computer Vision: Language and Vision
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Permanent Data Encoding (PDE) is a visual language framework designed for long-term, human-readable, and electrically independent knowledge preservation. By encoding semantic content into compact 2-3 character alphanumeric codes, paired with public dictionaries and rule-based expansion structures, PDE enables information to be visually interpreted and logically reconstructed without reliance on digital systems. Unlike QR codes or binary data, PDE offers a transparent and self-contained method of encoding meaning. This paper outlines the PDE syntax, dictionary protocol, use cases in disaster resilience and AI integration, and its implications as a cross-generational semantic infrastructure.

Problem

• Develops a visual language for long-term knowledge preservation
• Encodes semantic content into compact alphanumeric units
• Enables human-readable data without digital systems

Innovation

• Visual language for semantic compression
• 3-character alphanumeric encoding units
• Rule-based expansion and dictionaries