Dictionary of Meanings - Language-Independent Semantic System

DICTIONARY_OF_MEANINGS.md

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+# Dictionary of Meanings - Specification
+
+## Overview
+
+The Dictionary of Meanings is a formal type system for representing semantic meanings that enables language-independent fact representation, cross-language translation, and flexible phrase generation in multiple styles, dialects, and complexity levels.
+
+## Core Concepts
+
+### 1. Formal Type System
+
+The system is based on a hierarchical type system where:
+
+- **Meaning** (or **Type**): A fundamental semantic unit representing a single concept
+- **Submeaning** (or **Subtype**): A more specific or component meaning derived from a parent meaning
+- **Primitive Meaning**: An atomic, indivisible semantic unit (semantic prime)
+- **Composite Meaning**: A meaning composed of multiple submeanings
+
+This approach is inspired by:
+- Semantic decomposition (breaking complex meanings into semantic primitives)
+- Type theory (nested functions with defined input/output types)
+- Ontological semantics (formal concept definitions and relationships)
+- ConceptNet and WordNet (semantic networks and lexical databases)
+
+### 2. Meaning Decomposition
+
+Each meaning can be recursively decomposed into submeanings:
+
+```
+Meaning: "Run"
+├── Submeaning: "Move"
+│   ├── Submeaning: "Change Position"
+│   └── Submeaning: "Use Legs"
+└── Submeaning: "Fast"
+    └── Submeaning: "Speed > Walking"
+```
+
+This decomposition continues until semantic primitives are reached.
+
+## Architecture Components
+
+### 1. Meaning Database
+
+A structured database containing:
+
+#### Core Schema
+
+```
+Meaning {
+  id: UUID
+  name: String
+  type: MeaningType (PRIMITIVE | COMPOSITE)
+  category: OntologicalCategory (EVENT | STATE | PLACE | AMOUNT | THING | PROPERTY)
+  submeanings: Array<MeaningRelation>
+  metadata: {
+    complexity: Number
+    frequency: Number
+    domain: Array<String>
+  }
+}
+
+MeaningRelation {
+  target_meaning_id: UUID
+  relation_type: RelationType (IS_A | PART_OF | HAS_PROPERTY | CAUSES | REQUIRES)
+  weight: Number (0.0 - 1.0)
+}
+```
+
+### 2. Language Translation System
+
+Maps meanings to expressions in different languages:
+
+#### Translation Schema
+
+```
+LanguageMapping {
+  meaning_id: UUID
+  language_code: String (ISO 639-3)
+  expressions: Array<Expression>
+}
+
+Expression {
+  text: String
+  phonetic: String (IPA - International Phonetic Alphabet)
+  formality: FormalityLevel (VERY_FORMAL | FORMAL | NEUTRAL | INFORMAL | VERY_INFORMAL)
+  register: Array<Register> (TECHNICAL | LITERARY | COLLOQUIAL | SLANG)
+  frequency: Number
+  context_constraints: Array<String>
+}
+```
+
+### 3. Replacement Patterns
+
+Templates for converting meanings to phrases:
+
+#### Pattern Schema
+
+```
+ReplacementPattern {
+  id: UUID
+  name: String
+  input_meanings: Array<MeaningSlot>
+  output_template: Template
+  language_code: String
+  style: StyleDescriptor
+  constraints: Array<Constraint>
+}
+
+MeaningSlot {
+  position: Number
+  meaning_category: OntologicalCategory
+  role: SemanticRole (AGENT | PATIENT | INSTRUMENT | LOCATION | TIME)
+}
+
+Template {
+  structure: String (with placeholders: {0}, {1}, etc.)
+  transformations: Array<Transformation>
+}
+
+Transformation {
+  type: TransformationType (INFLECT | CONJUGATE | PLURALIZE | CASE)
+  parameters: Map<String, Any>
+}
+
+StyleDescriptor {
+  register: Register
+  formality: FormalityLevel
+  complexity: ComplexityLevel (SIMPLE | INTERMEDIATE | ADVANCED)
+  verbosity: VerbosityLevel (CONCISE | MODERATE | ELABORATE)
+  audience: AudienceType (CHILD | GENERAL | EXPERT)
+}
+```
+
+### 4. Fact Representation System
+
+Facts are represented as structured meaning combinations:
+
+```
+Fact {
+  id: UUID
+  predicate_meaning_id: UUID
+  arguments: Array<FactArgument>
+  modifiers: Array<FactModifier>
+  temporal: TemporalInfo
+  modal: ModalInfo
+  truth_value: Number (0.0 - 1.0)
+}
+
+FactArgument {
+  role: SemanticRole
+  meaning_id: UUID
+  value: Any (for concrete instances)
+}
+
+FactModifier {
+  type: ModifierType (NEGATION | INTENSIFICATION | ASPECT)
+  meaning_id: UUID
+}
+```
+
+## Key Features
+
+### 1. Language-Independent Representation
+
+Facts are stored using meaning IDs, independent of any specific language:
+
+```
+Example Fact: "The cat runs quickly"
+
+Represented as:
+{
+  predicate_meaning_id: "meaning:run",
+  arguments: [
+    {role: AGENT, meaning_id: "meaning:cat", determiner: DEFINITE}
+  ],
+  modifiers: [
+    {type: MANNER, meaning_id: "meaning:quick"}
+  ]
+}
+```
+
+### 2. Multi-Language Translation
+
+The same fact can be translated to any language without neural networks:
+
+```
+English: "The cat runs quickly"
+Spanish: "El gato corre rápidamente"
+French: "Le chat court rapidement"
+Russian: "Кошка быстро бежит"
+Japanese: "猫が速く走る"
+IPA: [ðə kæt rʌnz ˈkwɪkli]
+```
+
+All translations are generated from the same meaning-based representation using language-specific mapping tables.
+
+### 3. Style and Complexity Variation
+
+A single fact can be expressed in multiple styles:
+
+```
+Fact: [meaning:run(agent:meaning:cat, manner:meaning:quick)]
+
+Expressions:
+- Formal/Technical: "The feline exhibits rapid locomotion"
+- Standard: "The cat runs quickly"
+- Simple (child): "The cat goes fast"
+- Literary: "The nimble feline darts swiftly"
+- Concise: "Cat runs fast"
+- Elaborate: "The small domestic feline creature moves its legs in a swift running motion"
+```
+
+### 4. Personalized Vocabulary
+
+Expressions can be tailored to individual users:
+
+```
+User Vocabulary Profile:
+- known_meanings: Set<UUID>
+- preferred_complexity: ComplexityLevel
+- preferred_formality: FormalityLevel
+
+Expression Generation:
+1. Check meaning IDs against user's known_meanings
+2. If unknown meaning found:
+   - Substitute with known synonym, OR
+   - Decompose into known submeanings, OR
+   - Add definition/explanation
+3. Apply user's style preferences
+```
+
+### 5. Semantic Search and Reasoning
+
+The type system enables powerful semantic operations:
+
+```
+Operations:
+- Find all submeanings of X
+- Find all meanings containing submeaning Y
+- Calculate semantic distance between meanings
+- Find meanings with similar decomposition patterns
+- Reason about meaning relationships (if A is-a B, and B has-property C, then A has-property C)
+```
+
+## Implementation Considerations
+
+### Data Storage
+
+- **Graph Database** (e.g., Neo4j, ArangoDB): Natural fit for meaning relationships
+- **Document Database** (e.g., MongoDB): Flexible schema for meanings and patterns
+- **Relational Database** (e.g., PostgreSQL): Strong consistency for language mappings
+- **Hybrid Approach**: Graph for meanings, relational for translations
+
+### Performance Optimization
+
+1. **Caching**: Frequently used meaning decompositions and translations
+2. **Indexing**: Meaning IDs, language codes, semantic roles
+3. **Precomputation**: Common phrase patterns for each language
+4. **Lazy Loading**: Load submeanings only when needed
+
+### Extensibility
+
+1. **Open Schema**: Allow custom ontological categories and relation types
+2. **Plugin System**: Language-specific modules for morphology and syntax
+3. **Crowdsourcing**: Community contributions for language mappings
+4. **Version Control**: Track changes to meanings and relationships over time
+
+## Use Cases
+
+### 1. Machine Translation
+
+Traditional translation: Text → Neural Network → Text
+
+Meaning-based translation: Text → Meanings → Text
+- More interpretable
+- No training data needed for new language pairs
+- Consistent translations
+- Controllable style and formality
+
+### 2. Simplified Communication
+
+Generate age-appropriate or expertise-appropriate explanations:
+- Medical reports for patients vs. doctors
+- Technical documentation for beginners vs. experts
+- News articles for children vs. adults
+
+### 3. Language Learning
+
+- Show meaning decomposition to understand word components
+- Generate practice sentences at appropriate complexity level
+- Provide translations in learner's native language
+- Demonstrate how same meaning expressed differently across languages
+
+### 4. Accessibility
+
+- Generate simplified versions of complex texts
+- Provide definitions using only known vocabulary
+- Adjust reading level for cognitive disabilities
+- Support multiple modalities (text, speech, sign language via meaning representation)
+
+### 5. Knowledge Base Construction
+
+- Store facts in language-independent format
+- Query facts semantically rather than textually
+- Reason over facts using type system relationships
+- Integrate knowledge from multiple languages
+
+## Future Enhancements
+
+### 1. Multimodal Meanings
+
+Extend beyond text to include:
+- Visual representations (images, diagrams)
+- Auditory representations (sounds, music)
+- Gestural representations (sign language, body language)
+- Tactile representations (for accessibility)
+
+### 2. Context-Aware Generation
+
+Consider discourse context:
+- Previous statements in conversation
+- Shared knowledge between speaker and listener
+- Physical and social context
+- Pragmatic implications
+
+### 3. Emotional and Attitudinal Dimensions
+
+Add layers for:
+- Emotional valence (positive, negative, neutral)
+- Speaker attitude (certain, uncertain, ironic, emphatic)
+- Social relationships (power dynamics, familiarity)
+
+### 4. Temporal and Historical Evolution
+
+Track how meanings change over time:
+- Historical usage patterns
+- Semantic drift and shift
+- Etymology and meaning origin
+- Dialectal variations
+
+### 5. Integration with Neural Systems
+
+Hybrid approach:
+- Use meaning system for interpretable core
+- Use neural networks for ambiguity resolution
+- Use embeddings to suggest meaning relationships
+- Train models on meaning-annotated data
+
+## Related Work and References
+
+### Academic Foundations
+
+1. **Semantic Primitives**: Wierzbicka's Natural Semantic Metalanguage (NSM)
+2. **Formal Semantics**: Montague Grammar, Type Theory
+3. **Ontology**: Conceptual Semantics, Ontological Semantics
+4. **Lexical Resources**: WordNet, ConceptNet, FrameNet, BabelNet
+
+### Similar Projects
+
+1. **ConceptNet**: Open multilingual semantic network
+2. **WordNet**: Lexical database with semantic relations
+3. **Universal Dependencies**: Cross-linguistic grammatical relations
+4. **Abstract Meaning Representation (AMR)**: Semantic representation for sentences
+5. **Interlingua**: Language-independent meaning representation for translation
+
+### Key Differences
+
+This system emphasizes:
+- User-personalized vocabulary adaptation
+- Explicit style and complexity control
+- Recursive type decomposition
+- Direct generation without neural networks
+- Integration of IPA for pronunciation-based unified representation
+
+## Conclusion
+
+The Dictionary of Meanings provides a principled, extensible foundation for language-independent semantic representation. By combining formal type theory, semantic decomposition, and flexible generation patterns, it enables powerful applications in translation, communication, accessibility, and knowledge management while remaining interpretable and controllable.