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.
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Merge IT enriched institutions back into unified global dataset.
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"""
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import yaml
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from datetime import datetime, timezone
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def merge_it_enriched():
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print("=" * 80)
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print("🔀 Merging IT enriched data into unified dataset")
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print("=" * 80)
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# Load unified dataset
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print("\n📂 Loading unified dataset...")
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with open('data/instances/all/globalglam-20251111.yaml', 'r', encoding='utf-8') as f:
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all_institutions = yaml.safe_load(f)
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print(f" ✅ Loaded {len(all_institutions)} institutions")
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# Load enriched IT data
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print("\n📂 Loading IT enriched data...")
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with open('data/instances/italy/it_institutions_enriched_manual.yaml', 'r', encoding='utf-8') as f:
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it_enriched = yaml.safe_load(f)
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print(f" ✅ Loaded {len(it_enriched)} enriched IT institutions")
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# Create lookup by ID URL for quick matching
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it_by_id = {inst['id']: inst for inst in it_enriched}
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# Merge enriched data
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print("\n🔄 Merging enriched data...")
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merged_count = 0
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for i, inst in enumerate(all_institutions):
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inst_id = inst.get('id')
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if inst_id in it_by_id:
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# Preserve GHCID from original if it exists
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enriched_inst = it_by_id[inst_id].copy()
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if 'ghcid' in inst:
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enriched_inst['ghcid'] = inst['ghcid']
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if 'ghcid_uuid' in inst:
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enriched_inst['ghcid_uuid'] = inst['ghcid_uuid']
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if 'ghcid_uuid_sha256' in inst:
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enriched_inst['ghcid_uuid_sha256'] = inst['ghcid_uuid_sha256']
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if 'ghcid_numeric' in inst:
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enriched_inst['ghcid_numeric'] = inst['ghcid_numeric']
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# Replace with enriched version
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all_institutions[i] = enriched_inst
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merged_count += 1
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print(f" ✅ Merged: {inst['name']}")
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print(f"\n 📊 Total merged: {merged_count}")
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# Save unified dataset with timestamp
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timestamp = datetime.now(timezone.utc).strftime('%Y%m%d')
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output_path = f'data/instances/all/globalglam-{timestamp}.yaml'
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print(f"\n💾 Saving updated unified dataset to {output_path}...")
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with open(output_path, 'w', encoding='utf-8') as f:
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yaml.dump(all_institutions, f, allow_unicode=True, sort_keys=False, default_flow_style=False)
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print(" ✅ Saved")
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# Verify IT coverage
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it_institutions = [
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inst for inst in all_institutions
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if any(loc.get('country') == 'IT' for loc in inst.get('locations', []))
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]
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it_with_wikidata = sum(
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1 for inst in it_institutions
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if any(i.get('identifier_scheme') == 'Wikidata' for i in inst.get('identifiers', []))
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)
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print("\n" + "=" * 80)
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print("📊 VERIFICATION - IT Institutions in Unified Dataset")
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print("=" * 80)
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print(f"Total IT institutions: {len(it_institutions)}")
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print(f"With Wikidata identifiers: {it_with_wikidata}")
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print(f"Coverage: {it_with_wikidata/len(it_institutions)*100:.1f}%")
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if it_with_wikidata == len(it_institutions):
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print("\n✅ SUCCESS: 100% Wikidata coverage verified in unified dataset!")
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print("🇮🇹 Phase 1 Italy: COMPLETE")
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print("\n")
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if __name__ == '__main__':
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merge_it_enriched()
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