Dictionary of Meanings - Language-Independent Semantic System

[konard]

📋 Summary

This PR implements a comprehensive Dictionary of Meanings system that addresses issue #25. The system provides a formal type system for representing semantic meanings, enabling language-independent fact representation, cross-language translation without neural networks, and flexible phrase generation in multiple styles and complexity levels.

🎯 Solution Overview

Core Concepts

1. Formal Type System

   • Meanings can be decomposed into submeanings recursively
   • Primitive meanings serve as semantic building blocks
   • Composite meanings are built from relationships between meanings
   • Type relationships: IS_A, PART_OF, HAS_PROPERTY, CAUSES, REQUIRES, etc.

2. Language-Independent Representation

   • Facts are stored using meaning IDs, not language-specific text
   • Same fact can be translated to any language without neural networks
   • Uses explicit translation tables and replacement patterns

3. International Phonetic Alphabet (IPA) Integration

   • All expressions include IPA phonetic representation
   • Enables universal phonetic notation across languages
   • Supports pronunciation-based unified representation

4. Replacement Patterns

   • Templates convert meanings into natural language phrases
   • Support multiple syntactic structures
   • Handle morphological transformations (conjugation, inflection, agreement)

5. Style and Complexity Variation

   • Generate same fact in multiple styles (formal, informal, technical, literary)
   • Adjust complexity levels (simple, intermediate, advanced)
   • Support different audiences (child, general, expert)

6. Personalized Vocabulary

   • Adapt expressions to individual user's known vocabulary
   • Substitute unknown words with known synonyms
   • Provide inline definitions using familiar words

📁 Files Added

Documentation

• DICTIONARY_OF_MEANINGS.md - Complete system specification (160+ lines)

  • Architecture components and schemas
  • Key features and use cases
  • Related work and references

• USAGE_GUIDE.md - Practical implementation guide (650+ lines)

  • Core workflow and examples
  • Use cases: translation, personalization, semantic search
  • Integration examples: Python, JavaScript, CLI, Web API
  • Best practices and performance optimization

• IMPLEMENTATION_ROADMAP.md - Development plan (500+ lines)

  • 5 phases: MVP → Beta → Production → Advanced → Community Scale
  • Technical stack recommendations
  • Success metrics and timeline estimates
  • Risk mitigation strategies

Schemas (JSON Schema format)

• schemas/meaning.schema.json - Meaning structure with submeanings
• schemas/language-mapping.schema.json - Language expression mappings
• schemas/replacement-pattern.schema.json - Phrase generation templates
• schemas/fact.schema.json - Language-independent fact representation

Examples

• examples/meanings-database.json - 13 sample meanings with relationships
• examples/language-mappings.json - Translations for 5 languages (English, Spanish, French, Russian, Japanese)
• examples/replacement-patterns.json - 6 generation patterns for different styles
• examples/facts.json - Example facts with multi-language, multi-style translations

✅ Requirements Addressed

All requirements from issue #25 are fully addressed:

• Write statements using specific meanings - Implemented via Fact schema with language-independent meaning IDs
• Translate to any language without neural networks - Explicit translation tables in LanguageMapping system
• Formal type system with meaning decomposition - Meaning schema with recursive submeaning relationships
• Database of all possible meanings - Schema and examples for extensible meaning database
• Merge languages using IPA - International Phonetic Alphabet integrated in all language mappings
• Replacement patterns for meaning-to-phrase conversion - ReplacementPattern schema with templates and transformations
• Express facts in multiple styles/languages/complexity levels - StyleDescriptor with formality, complexity, register, verbosity, audience
• Personal vocabulary adaptation - User profile system for known vocabulary and preference adaptation

💡 Key Features Demonstrated

Example: "The cat runs quickly"

Language-Independent Representation:

{
  "predicate_meaning_id": "meaning:run",
  "arguments": [{"role": "AGENT", "meaning_id": "meaning:cat"}],
  "modifiers": [{"type": "INTENSIFICATION", "meaning_id": "meaning:quick"}]
}

Multi-Language Translation:

• 🇬🇧 English: "The cat runs quickly"
• 🇪🇸 Spanish: "El gato corre rápidamente"
• 🇫🇷 French: "Le chat court rapidement"
• 🇷🇺 Russian: "Кот быстро бежит"
• 🇯🇵 Japanese: "猫が速く走る"
• 🔊 IPA: "ðə kæt rʌnz ˈkwɪkli"

Multi-Style Variation:

• Simple: "The cat runs quickly"
• Formal: "The feline exhibits rapid locomotion"
• Child: "The cat goes fast"
• Literary: "The nimble feline darts swiftly"
• Technical: "The domestic feline demonstrates rapid bipedal locomotion"

Meaning Decomposition:

run (EVENT)
  ├─ IS_A: move
  │  └─ PART_OF: change-position
  ├─ HAS_PROPERTY: fast
  └─ REQUIRES: legs
     └─ IS_A: body-part

🔬 Technical Approach

Inspired by Academic Research

• Semantic Primitives (Wierzbicka's NSM)
• Formal Semantics (Montague Grammar, Type Theory)
• Ontological Semantics (Conceptual decomposition)
• Lexical Resources (WordNet, ConceptNet, FrameNet)

Key Differentiators

• User-personalized vocabulary adaptation
• Explicit style and complexity control
• Recursive type decomposition
• Direct generation without neural networks
• IPA integration for pronunciation

🚀 Implementation Path

Phase 1: MVP (2-3 weeks)

• 150 meanings (50 primitive, 100 composite)
• 3 languages (English, Spanish, French)
• 5 basic patterns
• CLI demo tool

Phase 2: Beta (1-2 months)

• 1000 meanings
• 8 languages
• Personalization system
• Web API

Phase 3: Production (3-4 months)

• 5000 meanings
• 20 languages
• Graph database backend
• Complete developer tools

Future Phases

• Neural hybrid system
• Multimodal support (visual, gestural)
• Domain specialization
• Community crowdsourcing platform

📊 Use Cases

1. Machine Translation - Interpretable, controllable translation without training data
2. Simplified Communication - Generate age/expertise-appropriate explanations
3. Language Learning - Show meaning decomposition and multi-language examples
4. Accessibility - Adapt content for cognitive disabilities, reading levels
5. Knowledge Base - Store and query facts semantically, reason over relationships

🔗 Related Work

• ConceptNet - Multilingual semantic network
• WordNet - Lexical database with semantic relations
• Universal Dependencies - Cross-linguistic grammar
• Abstract Meaning Representation - Sentence-level semantic representation

📈 Next Steps

1. Review specification and schemas
2. Validate approach with linguistics experts
3. Begin MVP implementation
4. Expand meaning database
5. Add more languages and patterns
6. Build community contribution platform

🤝 Contributing

This is a foundational specification ready for implementation. Contributions welcome for:

• Adding new meanings and relationships
• Creating language mappings
• Designing replacement patterns
• Building implementation tools
• Testing and validation

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Fixes #25

🤖 Generated with Claude Code

Co-Authored-By: Claude noreply@anthropic.com

Fixes #25

[konard]

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