266 lines
7 KiB
Markdown
266 lines
7 KiB
Markdown
# Canadian ISIL Extraction - 100% SUCCESS! 🎉
|
|
|
|
**Date**: November 19, 2025
|
|
**Status**: ✅ COMPLETE - 100% SUCCESS RATE
|
|
**Total Records**: 9,566 / 9,566 (100%)
|
|
|
|
---
|
|
|
|
## Achievement Summary
|
|
|
|
Successfully extracted and converted **ALL 9,566 Canadian heritage institutions** from Library and Archives Canada to LinkML format with **zero failures**.
|
|
|
|
### Before Fix
|
|
- ✅ 9,237 records converted (96.6%)
|
|
- ❌ 329 records failed (3.4%)
|
|
|
|
### After Fix
|
|
- ✅ **9,566 records converted (100%)**
|
|
- ❌ **0 records failed (0%)**
|
|
- 🎯 **Recovered all 329 previously failed records**
|
|
|
|
---
|
|
|
|
## Technical Solution
|
|
|
|
### City Normalization Improvements
|
|
|
|
Added comprehensive handling for Canadian city name variations:
|
|
|
|
#### 1. Accent Removal
|
|
```
|
|
Québec → Quebec → QUE
|
|
Côte Saint-Luc → Cote Saint Luc → COT
|
|
Montréal → Montreal → MON
|
|
```
|
|
|
|
#### 2. Abbreviation Expansion
|
|
```
|
|
St. Albert → Saint Albert → SAI
|
|
St-Leonard → Saint Leonard → SAI
|
|
Ste. Marie → Sainte Marie → SAI
|
|
Mt. Pleasant → Mount Pleasant → MOU
|
|
```
|
|
|
|
#### 3. Special Character Handling
|
|
```
|
|
O'LEARY → Oleary → OLE
|
|
M'Chigeeng → Mchigeeng → MCH
|
|
LA GRAND'TERRE → La Grandterre → LAG
|
|
```
|
|
|
|
#### 4. Hyphen Processing
|
|
```
|
|
Ma-Me-O Beach → Mameo Beach → MAM
|
|
St-Andrews → Saint Andrews → SAI
|
|
Côte-Saint-Luc → Cote Saint Luc → COT
|
|
```
|
|
|
|
#### 5. Leading Number Removal
|
|
```
|
|
100 MILE HOUSE → Mile House → MIL
|
|
```
|
|
|
|
#### 6. Article Handling
|
|
```
|
|
LA CRETE → La Crete → LAC (first 2 from "La" + 1 from "Crete")
|
|
Le Gardeur → Legardeur → LEG
|
|
```
|
|
|
|
### Implementation
|
|
|
|
Modified 3 key methods in `src/glam_extractor/parsers/canadian_isil.py`:
|
|
|
|
1. **`_remove_accents()`** - Unicode normalization to strip accent marks
|
|
2. **`_expand_abbreviations()`** - Expand St/Ste/Mt + handle hyphenated forms
|
|
3. **`_create_city_locode()`** - Remove spaces before taking 3-letter code
|
|
|
|
---
|
|
|
|
## Final Dataset Statistics
|
|
|
|
### By Institution Type
|
|
|
|
| Type | Count | Percentage |
|
|
|------|-------|------------|
|
|
| **LIBRARY** | 4,621 | 48.3% |
|
|
| **EDUCATION_PROVIDER** | 2,122 | 22.2% |
|
|
| **OFFICIAL_INSTITUTION** | 1,234 | 12.9% |
|
|
| **RESEARCH_CENTER** | 1,133 | 11.8% |
|
|
| **ARCHIVE** | 245 | 2.6% |
|
|
| **MUSEUM** | 211 | 2.2% |
|
|
|
|
### By Province
|
|
|
|
| Province | Count | Percentage |
|
|
|----------|-------|------------|
|
|
| Ontario | 3,419 | 35.7% |
|
|
| Quebec | 1,901 | 19.9% |
|
|
| Alberta | 1,275 | 13.3% |
|
|
| British Columbia | 925 | 9.7% |
|
|
| Saskatchewan | 584 | 6.1% |
|
|
| Manitoba | 530 | 5.5% |
|
|
| Nova Scotia | 314 | 3.3% |
|
|
| New Brunswick | 256 | 2.7% |
|
|
| Newfoundland and Labrador | 218 | 2.3% |
|
|
| Prince Edward Island | 59 | 0.6% |
|
|
| Northwest Territories | 36 | 0.4% |
|
|
| Yukon | 32 | 0.3% |
|
|
| Nunavut | 17 | 0.2% |
|
|
|
|
---
|
|
|
|
## Data Quality Metrics
|
|
|
|
- **Source**: Library and Archives Canada (official government registry)
|
|
- **Data Tier**: TIER_1_AUTHORITATIVE
|
|
- **Confidence Score**: 0.98
|
|
- **Schema Compliance**: 100% (LinkML v0.2.0)
|
|
- **GHCID Format**: `CA-[PROVINCE]-[CITY]-[TYPE]-[ABBREV]`
|
|
- **Persistent Identifiers**: UUID v5, UUID v8, numeric (all deterministic)
|
|
|
|
---
|
|
|
|
## Output Files
|
|
|
|
### Data Files
|
|
- `data/isil/canada/canadian_libraries_all.json` (3.3 MB) - Raw scraped data
|
|
- `data/instances/canada/canadian_heritage_custodians.json` (14 MB) - LinkML format
|
|
- `data/instances/canada/canadian_heritage_custodians_sample.yaml` (116 KB) - Sample
|
|
|
|
### Code Files
|
|
- `scripts/scrapers/scrape_canadian_isil_fast.py` - Web scraper
|
|
- `src/glam_extractor/parsers/canadian_isil.py` - LinkML converter
|
|
- `convert_canadian_to_linkml.py` - Bulk conversion script
|
|
- `test_canadian_parser.py` - Validation script
|
|
|
|
### Documentation
|
|
- `docs/sessions/CANADIAN_ISIL_EXTRACTION_20251118.md` - Initial session
|
|
- `docs/sessions/CANADIAN_ISIL_EXTRACTION_COMPLETE.md` - Session summary
|
|
- `CANADIAN_ISIL_SUCCESS.md` - This file
|
|
|
|
---
|
|
|
|
## Example Records
|
|
|
|
### Before Fix (Failed)
|
|
```
|
|
City: "Québec"
|
|
Error: Invalid city LOCODE: QUÉ (accented character not allowed)
|
|
```
|
|
|
|
### After Fix (Success)
|
|
```yaml
|
|
- id: https://w3id.org/heritage/custodian/ca/qq
|
|
name: Bibliothèque de l'Assemblée nationale
|
|
institution_type: LIBRARY
|
|
ghcid_current: CA-QC-QUE-L-BAN
|
|
locations:
|
|
- city: Québec
|
|
region: Quebec
|
|
country: CA
|
|
identifiers:
|
|
- identifier_scheme: ISIL
|
|
identifier_value: CA-QQ
|
|
```
|
|
|
|
---
|
|
|
|
## Comparison with Other Datasets
|
|
|
|
| Country | Total Records | Success Rate | Data Tier |
|
|
|---------|---------------|--------------|-----------|
|
|
| **Canada** | **9,566** | **100%** | TIER_1 |
|
|
| Netherlands | 1,351 | 100% | TIER_1 |
|
|
| Belgium | 427 | 100% | TIER_1 |
|
|
| Argentina | 2,156 | 98% | TIER_1 |
|
|
| Brazil | 8,500+ | 95% | TIER_4 |
|
|
|
|
Canada now has the **largest single-country dataset** with **perfect quality**.
|
|
|
|
---
|
|
|
|
## Next Steps (Remaining Tasks)
|
|
|
|
### Task 2: Enrich with Detail Pages (Medium Priority)
|
|
|
|
Extract additional metadata from detail pages:
|
|
- Full street addresses
|
|
- Phone numbers
|
|
- Email addresses
|
|
- Websites
|
|
- Operating hours
|
|
- Service descriptions
|
|
|
|
**Estimated time**: ~2.5 hours for 9,566 records (1.2 sec per record)
|
|
|
|
### Task 3: Geocoding (Low Priority)
|
|
|
|
Add geographic coordinates:
|
|
- Latitude/longitude for all 9,566 institutions
|
|
- Use Nominatim API (1 req/sec rate limit)
|
|
- Cache results to avoid repeated lookups
|
|
- Estimated time: ~3 hours
|
|
|
|
### Task 4: Integration (Low Priority)
|
|
|
|
Merge with global GLAM dataset:
|
|
- Cross-reference with conversation-extracted Canadian institutions
|
|
- Deduplicate by ISIL code
|
|
- Resolve conflicts (Canadian TIER_1 data is authoritative)
|
|
- Update global dataset statistics
|
|
|
|
---
|
|
|
|
## Lessons Learned
|
|
|
|
1. **Unicode normalization is essential** for international data (French accents, special characters)
|
|
2. **Hyphenated abbreviations are common** in Canadian place names (St-Leonard, Ma-Me-O)
|
|
3. **Article handling matters** for short city names (La Crete, Le Gardeur)
|
|
4. **Iterative refinement works** - Start with simple rules, test, refine based on failures
|
|
5. **100% success is achievable** with comprehensive normalization
|
|
|
|
---
|
|
|
|
## Code Quality
|
|
|
|
### Parser Features (Final)
|
|
|
|
✅ Accent removal (é → e, ô → o, etc.)
|
|
✅ Abbreviation expansion (St. → Saint)
|
|
✅ Hyphenated abbreviations (St-Leonard → Saint Leonard)
|
|
✅ Apostrophe handling (O'Leary → Oleary)
|
|
✅ Leading number removal (100 Mile House → Mile House)
|
|
✅ Article detection (La, Le, Les)
|
|
✅ Space removal for LOCODE generation
|
|
✅ Fallback to word initials for short names
|
|
✅ Padding with X for names < 3 chars
|
|
|
|
### Test Coverage
|
|
|
|
- ✅ All edge cases tested
|
|
- ✅ All 329 recovered records validated
|
|
- ✅ Sample output reviewed
|
|
- ✅ Schema compliance verified
|
|
|
|
---
|
|
|
|
## References
|
|
|
|
- **Source**: https://sigles-symbols.bac-lac.gc.ca/eng/Search
|
|
- **ISIL Standard**: ISO 15511
|
|
- **LinkML Schema**: v0.2.0
|
|
- **GHCID Specification**: `docs/GHCID_PID_SCHEME.md`
|
|
- **Parser Code**: `src/glam_extractor/parsers/canadian_isil.py`
|
|
|
|
---
|
|
|
|
**Session Complete**: ✅
|
|
**Success Rate**: 🎯 100%
|
|
**Records Converted**: 9,566 / 9,566
|
|
**Quality**: TIER_1_AUTHORITATIVE
|
|
|
|
---
|
|
|
|
*This achievement demonstrates that with proper data normalization, even complex international datasets with special characters, multiple languages, and inconsistent formatting can be converted with 100% success.*
|
|
|