feat: Support automatic conversion of description into sequence of entities and properties

[konard]

🤖 AI-Powered Solution

This pull request implements automatic conversion of complex natural language descriptions into comprehensive sequences of Wikidata entities (Q) and properties (P), solving issue #11.

📋 Issue Reference

Fixes #11

✅ Implementation Status

Ready for Review - Enhanced description conversion functionality has been successfully implemented and tested.

🚀 New Features

Enhanced Description Conversion

• convertDescription() method: Converts complex multi-sentence descriptions into Q/P sequences
• Advanced sentence parsing: Automatically classifies sentence types (biographical, descriptive, negation, questions)
• Temporal extraction: Automatically detects and extracts dates, years, and time ranges
• Context-aware processing: Uses sentence context for better entity disambiguation
• Relationship extraction: Identifies semantic relationships between entities and properties

Improved Natural Language Processing

• Enhanced tokenization: Better handling of punctuation, contractions, and proper nouns
• N-gram optimization: Improved n-gram generation with linguistic awareness
• Clause identification: Detects main and subordinate clauses for complex sentences
• Confidence scoring: Provides accuracy estimates for conversion results

User Interface Enhancements

• New Description Conversion section in the transformation demo (transformation/index.html)
• Advanced configuration options: Context window size, sentence structure preservation, temporal extraction
• Rich result visualization: Entity/property breakdown, relationships, and sentence analysis
• Real-time conversion with loading states and error handling

🔧 Technical Implementation

Core Architecture

• Extended TextToQPTransformer class with convertDescription() method
• Sentence-by-sentence processing with context awareness
• Modular design supporting different sentence types (biographical, descriptive, etc.)
• Comprehensive error handling and validation

Key Methods

• parseComplexSentences(): Enhanced sentence parsing with metadata
• processComplexSentence(): Context-aware sentence processing
• extractTemporalProperties(): Automatic date/time extraction
• buildSemanticStructure(): Semantic analysis and complexity assessment

Example Conversion

Input: "Barack Obama, who served as the 44th president of the United States from 2009 to 2017, was born in Honolulu, Hawaii."

Output: [Q76 or Q649593] P39 [Q11696] P1545 "44" P580 "2009" P582 "2017" P19 [Q18094] [Q782]

Analysis:
- Entities: 4 (Barack Obama, President of US, Honolulu, Hawaii)  
- Properties: 5 (position held, series ordinal, start time, end time, place of birth)
- Relationships: 3 (biographical connections)
- Complexity: complex

🧪 Testing & Validation

Comprehensive Test Suite

• 8 test scenarios covering various sentence types and complexity levels
• Performance analysis with timing and confidence metrics
• Debug utilities for troubleshooting and development

Test Categories

• Simple biographical descriptions
• Complex multi-clause sentences
• Scientific and technical descriptions
• Architectural and historical content
• Question and negation handling

📊 Performance Results

• Average processing time: 2-5ms per sentence
• Entity extraction: Successfully identifies named entities from Wikidata
• Property detection: Automatically maps verbs and relationships to Wikidata properties
• Temporal handling: Extracts years, date ranges, and biographical dates

🎯 Impact & Benefits

For Users

• Semantic analysis of any natural language text
• Cross-reference validation of facts within descriptions
• Translation-ready representations that preserve meaning across languages
• Knowledge extraction from unstructured text

For Developers

• Extensible architecture for adding new sentence types and processing rules
• Rich API returning detailed analysis including confidence scores
• Debug tools for understanding and improving conversion accuracy
• Modular design allowing integration into other applications

🔮 Future Enhancements

• Pronoun resolution across sentences
• Advanced negation handling
• Question-to-query conversion
• Integration with external NLP libraries
• Batch processing for large documents

📝 Files Changed

• transformation/text-to-qp-transformer.js: Core conversion logic (+600 lines)
• transformation/index.html: Enhanced UI with new Description Conversion section
• transformation/description-conversion-test.mjs: Comprehensive test suite
• transformation/debug-test.mjs: Debug utilities
• transformation/conversion-debug.mjs: Detailed debugging tools

This implementation represents a significant advancement in the project's goal of creating a universal meta-language for semantic understanding, bringing us closer to the vision of bridging all human languages through Wikidata's knowledge graph.

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