kempersc 3c80de87e0 add isil entries

2025-11-19 23:25:22 +01:00

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Belgian ISIL Integration - Complete ✅

Session Date: November 18, 2025
Status: COMPLETE
Dataset: 421 Belgian Heritage Institutions

Executive Summary

Successfully integrated 421 Belgian heritage institutions from the KBR (Royal Library of Belgium) ISIL registry into the GLAM project. The pipeline includes web scraping, LinkML parsing, location inference, Wikidata enrichment, and RDF export.

Data Pipeline Results

Stage	Output File	Records	Coverage	File Size
1. Scraping	`belgian_isil_detailed.csv`	421	100% ISIL codes	72.4 KB
2. Parsing	`belgium_isil_institutions.yaml`	421	LinkML-compliant	283.2 KB
3. Location Enrichment	`belgium_isil_institutions_enriched.yaml`	421	74.1% cities	287.1 KB
4. Wikidata Enrichment	`belgium_isil_institutions_wikidata.yaml`	421	2.4% Q-numbers	291.4 KB
5. RDF Export	`belgium_isil_institutions.ttl`	421	14,546 triples	673.0 KB

Dataset Composition

Institution Types

Libraries: 357 (84.8%)
Archives: 56 (13.3%)
Museums: 8 (1.9%)

Geographic Coverage

Unique Belgian Cities: 293
Institutions with City Data: 312 (74.1%)
Institutions with Coordinates: 1 (0.2%)

Identifier Coverage

ISIL Codes: 421 (100%)
Wikidata Q-numbers: 10 (2.4%)
VIAF IDs: 8 (1.9%)
Website URLs: 421 (100%)

Technical Implementation

Phase 1-2: Web Scraping ✅

Script: scripts/scrapers/scrape_belgian_isil.py (existing from previous session)

Source: https://isil.kbr.be/

Method:

BeautifulSoup HTML parsing
Institution detail page extraction
Multi-field capture (name, code, type, parent org, accessibility)

Phase 3-4: LinkML Parsing ✅

Parser: src/glam_extractor/parsers/belgian_isil.py

Features:

358 lines, 89% test coverage
18 passing unit tests
GHCID generation with deterministic UUIDs
Full provenance tracking

Key Functions:

class BelgianISILParser:
    def parse_file(csv_path: Path) -> List[HeritageCustodian]
    def parse_row(row: dict) -> HeritageCustodian
    def _normalize_institution_type(raw_type: str) -> InstitutionTypeEnum
    def _extract_alternative_names(name: str) -> List[str]

Phase 6: Location Enrichment ✅

Script: scripts/enrich_belgian_locations.py

Method: Regex pattern matching on institution names

Patterns:

"Bibliotheek [City]" → City
"Bib [City]" → City  
"Archief [City]" → City
"([City])" → City  # Parenthetical city names

Results:

312/421 institutions (74.1%) now have city data
293 unique cities identified
Remaining 25.9% have generic/branded names without clear city indicators

Phase 7: Wikidata Enrichment ✅

Script: scripts/enrich_belgian_wikidata.py

Method: SPARQL query to Wikidata for ISIL code matches (P791)

Query Strategy:

SELECT ?item ?itemLabel ?viaf ?founded ?coordinate ?altLabel WHERE {
  VALUES ?isilCode { "BE-A2000" "BE-KBR00" ... }
  ?item wdt:P791 ?isilCode .
  OPTIONAL { ?item wdt:P214 ?viaf }
  OPTIONAL { ?item wdt:P571 ?founded }
  OPTIONAL { ?item wdt:P625 ?coordinate }
}

Results:

10 Q-numbers added (2.4% coverage)
8 VIAF IDs added
9 founding dates added
1 coordinate pair added

Notable Matches:

BE-KBR00: Royal Library of Belgium → Q383931
BE-TEN00: Royal Museum for Central Africa → Q779703
BE-A2003: Royal Institute for Cultural Heritage → Q2235462
BE-BUE01: Groeningemuseum → Q1948674

Low Coverage Reason: Most Belgian institutions don't have ISIL codes (P791) registered in Wikidata. This is typical for local/municipal libraries.

Phase 8: RDF Export ✅

Script: scripts/export_belgian_rdf.py

Exporter: src/glam_extractor/exporters/rdf_exporter.py (existing)

Ontology Integration:

Schema.org: Web discoverability (schema:Library, schema:Museum)
CIDOC-CRM: Museum metadata (cidoc:E74_Group)
RiC-O: Archival standards (rico:CorporateBody, rico:Identifier)
W3C ORG: Organizational structure (org:Organization)
PROV-O: Provenance tracking (prov:Entity, prov:Activity)
GHCID: Custom heritage custodian vocabulary

RDF Statistics:

14,546 total triples
1,604 unique subjects
31 unique predicates
3,132 unique objects
34.6 triples per institution (average)

Sample RDF (Royal Library of Belgium):

<BE-KBR00> a schema1:Library,
        schema1:Organization,
        org:Organization,
        prov:Entity,
        ghcid:HeritageCustodian ;
    dcterms:identifier "312739455", "BE-KBR00", "Q383931" ;
    schema1:alternateName "Bibliothèque Royale de Belgique",
        "Koninklijke Bibliotheek van België" ;
    schema1:name "Koninklijke Bibliotheek van België(Bibliothèque Royale de Belgique)" ;
    schema1:sameAs <https://viaf.org/viaf/312739455>,
        <https://www.wikidata.org/wiki/Q383931> ;
    schema1:location _:BrusselsLocation ;
    prov:generatedAtTime "2025-11-18T15:15:51.552783+00:00"^^xsd:dateTime .

Key Technical Discoveries

1. LinkML Enum Handling

Issue: LinkML permissive enums are objects, not strings (Pydantic v1 behavior)

Solution: Convert to string in RDF exporter:

inst_type_str = str(institution_type)  # "LIBRARY", "ARCHIVE", etc.

2. YAML Record Splitting

Issue: LinkML dumper concatenates records without --- separators

Pattern: Split on \n(?=id: BE-) regex to find record boundaries

records_text = re.split(r'\n(?=id: BE-)', yaml_content)

3. Wikidata ISIL Sparseness

Finding: Only 2.4% of Belgian institutions have ISIL codes in Wikidata

Implication: Future enrichment should use name + city fuzzy matching instead of relying solely on ISIL code (P791) queries

Data Quality Assessment

Tier 1: Authoritative Data ✅

ISIL codes: 100% coverage from official KBR registry
Institution names: Verified from source website
Website URLs: Direct from registry

Provenance:

provenance:
  data_source: CSV_REGISTRY
  data_tier: TIER_1_AUTHORITATIVE
  extraction_method: "BelgianISILParser with GHCID generation (scraped from KBR registry)"

Tier 3: Crowd-Sourced Data

Wikidata Q-numbers: 2.4% coverage (10 institutions)
VIAF IDs: 1.9% coverage (8 institutions)
Founding dates: 2.1% coverage (9 institutions)

Limitation: Most local Belgian libraries lack Wikidata presence

Tier 4: Inferred Data

City names: 74.1% coverage (312 institutions)
Method: Regex pattern matching on institution names
Confidence: Variable (0.85-0.95 for clear patterns)

Files Created This Session

Scripts

scripts/enrich_belgian_locations.py (NEW)
- Regex-based city extraction from institution names
- 312/421 institutions enriched (74.1%)
scripts/enrich_belgian_wikidata.py (NEW)
- SPARQL batch queries (100 codes per query)
- 10 Wikidata matches found
scripts/export_belgian_rdf.py (NEW)
- RDF/Turtle serialization
- Multi-ontology integration

Data Files

data/instances/belgium_isil_institutions.yaml (283.2 KB)
- Base LinkML export from CSV parsing
data/instances/belgium_isil_institutions_enriched.yaml (287.1 KB)
- Location-enriched version
data/instances/belgium_isil_institutions_wikidata.yaml (291.4 KB)
- Wikidata-enriched version (final YAML)
data/rdf/belgium_isil_institutions.ttl (673.0 KB)
- RDF/Turtle export with 14,546 triples

Documentation

docs/sessions/SESSION_SUMMARY_20251118_BELGIAN_ISIL.md (previous session)
- Phases 1-5 (scraping, parsing, initial export)
BELGIAN_ISIL_COMPLETE.md (this document)
- Complete pipeline documentation

Validation Results

RDF Validation ✅

python3 -c "from rdflib import Graph; g = Graph(); g.parse('data/rdf/belgium_isil_institutions.ttl', format='turtle'); print(f'Valid: {len(g)} triples')"

Output: Valid: 14,546 triples

Entity Type Distribution

Entity Type	Count	Notes
ghcid:Identifier	449	ISIL codes + Wikidata + VIAF + Website
schema:Organization	421	All institutions
schema:Library	357	84.8% of institutions
schema:Place	312	Location objects (74.1% coverage)
rico:CorporateBody	56	Archives
cidoc:E74_Group	8	Museums

Linkage Statistics

schema:sameAs links: 18 (10 Wikidata + 8 VIAF)
Location relationships: 312 (74.1%)
Identifier relationships: 421 (100%)
Provenance links: 421 (100%)

Future Enhancement Opportunities

1. Improve Wikidata Coverage

Method: Fuzzy name + city matching instead of ISIL-only

Potential: Could match 50-100 more institutions (12-25% coverage)

Script to create: scripts/enrich_belgian_wikidata_fuzzy.py

SPARQL approach:

SELECT ?item ?itemLabel WHERE {
  ?item wdt:P31/wdt:P279* wd:Q7075 .  # instance of library
  ?item wdt:P17 wd:Q31 .               # country: Belgium
  ?item wdt:P131*/wdt:P131* wd:[City] . # located in city
  SERVICE wikibase:label { bd:serviceParam wikibase:language "nl,fr,en" }
}

2. Geocoding with Nominatim

Target: 312 institutions with city names but no coordinates

Method: Nominatim API with rate limiting (1 req/sec)

Expected time: ~6 minutes for 312 cities

Script to create: scripts/geocode_belgian_institutions.py

3. Add More Identifiers

Potential identifier sources:

GND (German National Library): German-language institutions
BnF (French National Library): Francophone institutions
ULAN (Getty): Art museums and galleries

4. Cross-Link with Europeana

Integration: Check if Belgian institutions contribute to Europeana

API: Europeana Search API (institution provider queries)

Benefit: Link to digitized collections

5. Archives Portal Europe (APE) Integration

Target: 56 Belgian archives

API: APEx API for archival metadata

Benefit: Connect to EAD finding aids

Comparison with Other Datasets

Dutch ISIL Registry

Size: 364 institutions (Belgium: 421)
Wikidata coverage: Higher (~10%)
Location data: 100% (Belgium: 74.1%)

Austrian ISIL (in progress)

Status: Data extraction pending
Expected size: ~200 institutions

Conclusion

Belgium has better ISIL coverage (421 vs NL 364) but lower Wikidata linkage. Location enrichment is effective but could be improved with geocoding.

Usage Examples

1. Load Belgian Institutions in Python

from pathlib import Path
import yaml
from glam_extractor.models import HeritageCustodian

# Load from YAML
with open('data/instances/belgium_isil_institutions_wikidata.yaml', 'r') as f:
    institutions = yaml.safe_load_all(f)
    belgian_libs = [HeritageCustodian(**inst) for inst in institutions]

# Filter by type
libraries = [i for i in belgian_libs if i.institution_type == "LIBRARY"]
print(f"Belgian libraries: {len(libraries)}")

2. Query RDF with SPARQL

from rdflib import Graph

g = Graph()
g.parse('data/rdf/belgium_isil_institutions.ttl', format='turtle')

# Find all libraries in Brussels
query = """
PREFIX schema: <http://schema.org/>
PREFIX ghcid: <https://w3id.org/heritage/custodian/>

SELECT ?inst ?name WHERE {
  ?inst a schema:Library ;
        schema:name ?name ;
        ghcid:location ?loc .
  ?loc ghcid:city "Brussel" .
}
"""

for row in g.query(query):
    print(f"{row.inst}: {row.name}")

3. Export to JSON-LD

from glam_extractor.exporters.rdf_exporter import RDFExporter

exporter = RDFExporter()
# Add institutions...
jsonld = exporter.graph.serialize(format='json-ld')
print(jsonld)

Test Coverage

Parser Tests ✅

File: tests/parsers/test_belgian_isil.py

Coverage: 89%

Test Count: 18 tests, all passing

Key Tests:

Institution type normalization
Alternative name extraction
GHCID generation
Identifier parsing
Provenance metadata

Integration Tests (Suggested)

File to create: tests/integration/test_belgian_pipeline.py

Tests needed:

def test_full_pipeline():
    """Test scraping → parsing → enrichment → RDF export."""
    
def test_rdf_validation():
    """Ensure RDF syntax is valid."""
    
def test_identifier_linkage():
    """Verify Wikidata/VIAF sameAs links."""

Lessons Learned

1. Wikidata ISIL Property (P791) is Sparse

Finding: Only 2.4% of institutions have ISIL codes in Wikidata

Recommendation: Always use multi-strategy matching:

Try ISIL code first (fast, authoritative)
Fall back to name + city fuzzy matching
Manual review for ambiguous cases

2. Location Inference is Effective for European Data

Finding: 74% coverage from name patterns alone

Reason: European naming conventions often include city names

Limitation: Won't work for institutions with:

Branded names ("The Reading Tree")
Abbreviations without expansion
Generic names ("Central Library")

3. LinkML Enum Handling Requires Type Conversion

Issue: LinkML permissive enums are objects, not plain strings

Solution: Always convert to string when using as dict keys or in comparisons:

inst_type_str = str(institution.institution_type)

4. YAML Record Splitting for LinkML Output

Issue: LinkML YAML dumper doesn't insert --- separators between records

Solution: Use regex split pattern: \n(?=id: BE-)

Alternative: Use JSON-LD for multi-record exports (cleaner structure)

Project Status

✅ Completed Phases

Phase 1-2: Web scraping (421 institutions)
Phase 3-4: LinkML parsing and validation
Phase 5: Initial YAML export
Phase 6: Location enrichment (74.1% coverage)
Phase 7: Wikidata enrichment (2.4% coverage)
Phase 8: RDF/Turtle export (14,546 triples)

🎯 Next Steps (Optional)

Geocoding with Nominatim (312 cities → lat/lon)
Wikidata fuzzy matching (increase coverage to 15-25%)
Europeana integration (check collection contributions)
Archives Portal Europe linkage (56 archives)
GND/BnF identifier enrichment (German/French institutions)

📊 Overall Project Progress

Belgium is now the second fully integrated country in the GLAM project:

✅ Netherlands: 1,351 institutions (ISIL + Dutch Orgs CSV)
✅ Belgium: 421 institutions (ISIL registry)
🔄 Austria: In progress (ISIL extraction)
📋 Others: 60+ countries in conversation JSONs (pending extraction)

Contact and Credits

Data Source: Koninklijke Bibliotheek van België / Bibliothèque Royale de Belgique
Registry URL: https://isil.kbr.be/
ISIL Standard: ISO 15511:2019

Project: GLAM Data Extraction (Global Heritage Custodian Identification)
Repository: /Users/kempersc/apps/glam
Session Date: November 18, 2025

Documentation:

Schema: schemas/heritage_custodian.yaml (LinkML v0.2.1)
Agents Guide: AGENTS.md
Persistent IDs: docs/PERSISTENT_IDENTIFIERS.md

Appendix: Command Reference

Run Full Pipeline

# 1. Scraping (already done)
python3 scripts/scrapers/scrape_belgian_isil.py

# 2. Parsing
python3 scripts/parse_belgian_isil.py

# 3. Location enrichment
python3 scripts/enrich_belgian_locations.py

# 4. Wikidata enrichment
python3 scripts/enrich_belgian_wikidata.py

# 5. RDF export
python3 scripts/export_belgian_rdf.py

Validation

# Validate RDF syntax
python3 -c "from rdflib import Graph; g = Graph(); g.parse('data/rdf/belgium_isil_institutions.ttl'); print(f'{len(g)} triples')"

# Run parser tests
pytest tests/parsers/test_belgian_isil.py -v

# Check YAML syntax
yamllint data/instances/belgium_isil_institutions_wikidata.yaml

Statistics

# Count institutions
grep -c "^id: BE-" data/instances/belgium_isil_institutions_wikidata.yaml

# Count by type
grep "institution_type:" data/instances/belgium_isil_institutions_wikidata.yaml | sort | uniq -c

# Count with Wikidata
grep -c "identifier_scheme: Wikidata" data/instances/belgium_isil_institutions_wikidata.yaml

Status: ✅ PIPELINE COMPLETE
Last Updated: November 18, 2025
Next Session: Austrian ISIL Integration or Geocoding Enhancement

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