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glam/CANADIAN_ISIL_SUCCESS.md
kempersc 3c80de87e0 add isil entries
2025-11-19 23:25:22 +01:00

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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)

- 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

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.