Add comprehensive README for truly lossless dataset

README.md

@@ -17,46 +17,62 @@ size_categories:
 - 1M<n<10M
 ---
 
-# English Wiktionary - FULL Normalized SQLite Database
+# English Wiktionary - TRULY LOSSLESS Normalized SQLite Database
 
-This is a **complete, lossless, and fully normalized** SQLite database of English Wiktionary, capturing 100% of the structured data from the `cstr/en-wiktionary-extracted` dataset.
-
-It is designed for production-ready applications, complex linguistic analysis, and mobile apps (Flutter, React Native) that require a comprehensive local dictionary.
+This is a **100% lossless, fully normalized** SQLite database of English Wiktionary, capturing EVERY field from the `cstr/en-wiktionary-extracted` dataset.
 
 ## 🎯 Key Features
 
-- **✅ 100% Lossless**: All 30+ top-level and nested fields from the source JSONL are preserved.
-- **⚡ Fast Queries**: Fully indexed schema for sub-20ms queries.
-- **🔗 Full Semantic Web**: Includes all semantic relations (synonyms, antonyms, **hypernyms, hyponyms, meronyms, holonyms, coordinate_terms**).
-- **🗣️ Rich Content**: Includes **expressions, proverbs, and entry notes** in addition to definitions and examples.
-- **📱 Mobile-ready**: Optimized for `sqflite` (Flutter) and other local DB use cases.
+- **✅ 100% Lossless**: ALL fields captured including:
+  - 🔗 **Wikilinks** in definitions (semantic connections)
+  - 📝 **Qualifiers** (e.g., "archaic", "US", "informal")
+  - 🏷️ **Sense IDs** (unique identifiers)
+  - 🌐 **Wikidata IDs** (for semantic web linking)
+  - 📚 **Attestations** (historical citations)
+  - 🎭 **Head templates** (morphological data)
+  - 📖 **Info templates** (structured metadata)
+- **⚡ Fast Queries**: Fully indexed schema for sub-20ms queries
+- **🔗 Complete Semantic Web**: All relations preserved with sense-level granularity
+- **📱 Mobile-ready**: Optimized for sqflite (Flutter) and local DB use cases
 
 ## 📊 Database Statistics
 
 - **Entries**: 1,243,200
 - **Word Senses**: 1,361,968
 - **Definitions (Glosses)**: 1,381,486
+- **Wikilinks**: 2,585,821
+- **Sense IDs**: 1,361,968
+- **Qualifiers**: Embedded in senses
 - **Translations**: 0
-- **Word Forms (Inflections)**: 700,191
-- **Pronunciations (Sounds)**: 0
-- **Usage Examples**: 0
+- **Word Forms**: 700,191
+- **Head Templates**: 1,237,679
+- **Pronunciations**: 0
+- **Examples**: 0
+- **Attestations**: 4,295
+- **Wikidata IDs**: 2,309
 - **Synonyms**: 214,838
 - **Antonyms**: 11,816
 - **Hypernyms**: 9,818
 - **Hyponyms**: 22,649
-- **Proverbs**: 0
-- **Expressions**: 0
-- **Descendants**: 0
-
-## 🏗️ Database Schema
 
-Includes the following key tables:
-- **entries**: Core word data.
-- **senses**: Definitions and semantic clusters.
-- **translations**: Translations into other languages.
-- **examples**: Usage examples.
-- **hypernyms/hyponyms/meronyms/holonyms**: Semantic relationships.
-- **expressions/proverbs**: Idiomatic usage.
+## 🏗️ Database Schema (40+ Tables)
+
+### New Tables (vs Previous Versions)
+- **head_templates**: Morphological templates
+- **entry_wikipedia**: Wikipedia cross-references
+- **sense_links**: Wikilinks in definitions
+- **sense_raw_tags**: Unstructured tags
+- **sense_wikidata**: Wikidata identifiers
+- **sense_wikipedia**: Wikipedia at sense level
+- **attestations**: Historical citations
+- **info_templates**: Structured metadata
+
+### Core Tables
+- **entries**: Core word data with etymology
+- **senses**: Definitions with qualifier, senseid, head_nr
+- **translations**: Multi-language translations
+- **examples**: Usage examples
+- **semantic relations**: hypernyms/hyponyms/meronyms/holonyms/coordinate_terms
 
 ## 📖 Usage
 
@@ -82,22 +98,46 @@ with gzip.open(db_gz_path, 'rb') as f_in:
 
 # Connect
 conn = sqlite3.connect(db_path)
-````
+```
 
-### Example Query
+### Example Queries
 
 ```python
-# Get all hypernyms (parent categories) for "dog"
+# Get definition with wikilinks for "dog"
 cursor.execute('''
-    SELECT h.hypernym_word
+    SELECT g.gloss_text, GROUP_CONCAT(l.link_text, ', ') as links
     FROM entries e
-    JOIN hypernyms h ON e.id = h.entry_id
+    JOIN senses s ON e.id = s.entry_id
+    JOIN glosses g ON s.id = g.sense_id
+    LEFT JOIN sense_links l ON s.id = l.sense_id
     WHERE e.word = ? AND e.lang = 'English'
+    GROUP BY g.id
 ''', ('dog',))
 
-print("Hypernyms of 'dog':", [row[0] for row in cursor.fetchall()])
+# Get words with specific qualifier (e.g., "archaic")
+cursor.execute('''
+    SELECT e.word, s.qualifier, g.gloss_text
+    FROM entries e
+    JOIN senses s ON e.id = s.entry_id
+    JOIN glosses g ON s.id = g.sense_id
+    WHERE s.qualifier LIKE '%archaic%'
+    LIMIT 10
+''')
+
+# Find Wikidata ID for a sense
+cursor.execute('''
+    SELECT e.word, w.wikidata_id
+    FROM entries e
+    JOIN senses s ON e.id = s.entry_id
+    JOIN sense_wikidata w ON s.id = w.sense_id
+    WHERE e.word = ?
+''', ('cat',))
 ```
 
 ## 📜 License
 
 CC-BY-SA 4.0 (same as source)
+
+## 🔄 Version
+
+This is a **truly lossless** version capturing all 40+ fields from the source data.