e5a532a8bc
- Introduced `test_nlp_extractor.py` with unit tests for the InstitutionExtractor, covering various extraction patterns (ISIL, Wikidata, VIAF, city names) and ensuring proper classification of institutions (museum, library, archive). - Added tests for extracted entities and result handling to validate the extraction process. - Created `test_partnership_rdf_integration.py` to validate the end-to-end process of extracting partnerships from a conversation and exporting them to RDF format. - Implemented tests for temporal properties in partnerships and ensured compliance with W3C Organization Ontology patterns. - Verified that extracted partnerships are correctly linked with PROV-O provenance metadata.
64 lines
2 KiB
Python
64 lines
2 KiB
Python
#!/usr/bin/env python3
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"""
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Debug script to test Wikidata enrichment for specific institutions.
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"""
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import sys
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sys.path.insert(0, '/Users/kempersc/apps/glam')
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from scripts.enrich_tunisia_wikidata_validated import search_wikidata_with_validation
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# Test cases from previous session findings
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test_cases = [
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{
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'name': 'Kerkouane Archaeological Site and Museum',
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'type': 'MUSEUM',
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'city': 'Kerkouane',
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'expected_qid': 'Q3329437',
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'expected_label': 'musée archéologique de Kerkouane',
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'issue': 'Match score 67% (below 70% threshold)'
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},
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{
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'name': 'Diocesan Library of Tunis',
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'type': 'LIBRARY',
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'city': 'Tunis',
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'expected_qid': 'Q28149782',
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'expected_label': 'bibliothèque diocésaine de Tunis',
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'issue': 'Instance type Q105338594 not in mapping'
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}
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]
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print("Wikidata Enrichment Debug Test")
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print("=" * 60)
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for i, test in enumerate(test_cases, 1):
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print(f"\nTest {i}: {test['name']}")
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print(f" Type: {test['type']}")
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print(f" City: {test['city']}")
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print(f" Expected: {test['expected_qid']} - {test['expected_label']}")
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print(f" Previous issue: {test['issue']}")
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print(f"\n Running search...")
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result = search_wikidata_with_validation(
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name=test['name'],
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inst_type=test['type'],
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city=test['city']
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)
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if result:
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print(f" ✅ FOUND!")
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print(f" QID: {result['qid']}")
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print(f" Name: {result.get('name', 'N/A')}")
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print(f" Entity type: {result.get('entity_type', 'N/A')}")
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print(f" Match score: {result.get('match_score', 0):.1f}%")
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if result['qid'] == test['expected_qid']:
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print(f" ✅ CORRECT MATCH! Threshold fix worked.")
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else:
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print(f" ⚠️ Different entity matched (expected {test['expected_qid']})")
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else:
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print(f" ❌ NOT FOUND")
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print(f" May still be below 65% threshold or type validation failing")
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print("\n" + "=" * 60)
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print("Debug test complete")
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