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.
393 lines
14 KiB
Python
393 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Combine Brazilian, Chilean, and Mexican GLAM institution datasets into a unified collection.
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This script:
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1. Loads the three master YAML files
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2. Validates and merges them into a single collection
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3. Generates statistics about the combined dataset
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4. Outputs a unified YAML file
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Input files:
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- data/instances/brazilian_institutions.yaml (97 institutions)
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- data/instances/chilean_institutions.yaml (90 institutions)
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- data/instances/mexican_institutions.yaml (117 institutions)
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Output:
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- data/instances/latin_american_institutions.yaml (304 institutions combined)
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- data/instances/latin_american_combination_report.md
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"""
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import yaml
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from pathlib import Path
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from datetime import datetime, timezone
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from collections import Counter
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from typing import List, Dict, Any
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# File paths
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BASE_DIR = Path(__file__).parent.parent
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INSTANCES_DIR = BASE_DIR / "data" / "instances"
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INPUT_FILES = {
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"brazilian": INSTANCES_DIR / "brazilian_institutions.yaml",
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"chilean": INSTANCES_DIR / "chilean_institutions.yaml",
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"mexican": INSTANCES_DIR / "mexican_institutions.yaml"
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}
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OUTPUT_FILE = INSTANCES_DIR / "latin_american_institutions.yaml"
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REPORT_FILE = INSTANCES_DIR / "latin_american_combination_report.md"
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def load_yaml_file(file_path: Path) -> List[Dict[str, Any]]:
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"""Load a YAML file and return the list of institutions."""
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print(f"Loading {file_path.name}...")
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with open(file_path, 'r', encoding='utf-8') as f:
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data = yaml.safe_load(f)
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# Handle both direct lists and documents with metadata headers
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if isinstance(data, list):
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return data
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elif isinstance(data, dict):
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# If it's a dict, it might be wrapped - look for common keys
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if 'institutions' in data:
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return data['institutions']
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else:
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raise ValueError(f"Unexpected YAML structure in {file_path}")
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else:
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raise ValueError(f"Unexpected YAML type in {file_path}: {type(data)}")
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def get_country_from_id(institution_id: str) -> str:
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"""Extract country code from institution ID."""
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# IDs are like: https://w3id.org/heritage/custodian/br/...
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parts = institution_id.split('/')
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if len(parts) >= 6:
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return parts[5].upper()
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return "UNKNOWN"
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def has_coordinates(institution: Dict[str, Any]) -> bool:
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"""Check if institution has geographic coordinates."""
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locations = institution.get('locations', [])
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if not locations:
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return False
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for loc in locations:
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if loc.get('latitude') is not None and loc.get('longitude') is not None:
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return True
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return False
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def analyze_institutions(institutions: List[Dict[str, Any]], country_name: str) -> Dict[str, Any]:
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"""Analyze a set of institutions and return statistics."""
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stats = {
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'country': country_name,
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'total': len(institutions),
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'geocoded': sum(1 for inst in institutions if has_coordinates(inst)),
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'with_identifiers': sum(1 for inst in institutions if inst.get('identifiers')),
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'with_platforms': sum(1 for inst in institutions if inst.get('digital_platforms')),
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'with_collections': sum(1 for inst in institutions if inst.get('collections')),
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'institution_types': Counter(),
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'regions': Counter(),
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'cities': Counter()
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}
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for inst in institutions:
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# Count institution types
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inst_type = inst.get('institution_type', 'UNKNOWN')
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stats['institution_types'][inst_type] += 1
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# Count regions and cities
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for loc in inst.get('locations', []):
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if loc.get('region'):
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stats['regions'][loc['region']] += 1
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if loc.get('city'):
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stats['cities'][loc['city']] += 1
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stats['geocoding_rate'] = (stats['geocoded'] / stats['total'] * 100) if stats['total'] > 0 else 0
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stats['unique_regions'] = len(stats['regions'])
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stats['unique_cities'] = len(stats['cities'])
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return stats
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def combine_datasets() -> None:
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"""Main function to combine datasets."""
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print("=" * 60)
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print("Latin American GLAM Institutions Dataset Combination")
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print("=" * 60)
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print()
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# Load all three datasets
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datasets = {}
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all_institutions = []
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for country_key, file_path in INPUT_FILES.items():
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if not file_path.exists():
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print(f"ERROR: {file_path} not found!")
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return
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institutions = load_yaml_file(file_path)
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datasets[country_key] = institutions
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all_institutions.extend(institutions)
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print(f" Loaded {len(institutions)} institutions from {file_path.name}")
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print(f"\nTotal institutions loaded: {len(all_institutions)}")
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# Analyze each dataset
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print("\n" + "=" * 60)
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print("Individual Dataset Statistics")
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print("=" * 60)
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country_stats = {}
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for country_key, institutions in datasets.items():
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country_name = country_key.capitalize()
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stats = analyze_institutions(institutions, country_name)
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country_stats[country_key] = stats
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print(f"\n{country_name}:")
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print(f" Total institutions: {stats['total']}")
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print(f" Geocoded: {stats['geocoded']} ({stats['geocoding_rate']:.1f}%)")
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print(f" Unique regions: {stats['unique_regions']}")
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print(f" Unique cities: {stats['unique_cities']}")
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print(f" With identifiers: {stats['with_identifiers']}")
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print(f" With platforms: {stats['with_platforms']}")
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print(f" Top 3 institution types:")
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for inst_type, count in stats['institution_types'].most_common(3):
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print(f" - {inst_type}: {count}")
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# Analyze combined dataset
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print("\n" + "=" * 60)
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print("Combined Dataset Statistics")
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print("=" * 60)
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combined_stats = analyze_institutions(all_institutions, "Latin America")
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print(f"\nTotal institutions: {combined_stats['total']}")
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print(f"Geocoded: {combined_stats['geocoded']} ({combined_stats['geocoding_rate']:.1f}%)")
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print(f"Unique regions: {combined_stats['unique_regions']}")
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print(f"Unique cities: {combined_stats['unique_cities']}")
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print(f"With identifiers: {combined_stats['with_identifiers']}")
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print(f"With digital platforms: {combined_stats['with_platforms']}")
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print(f"With collections: {combined_stats['with_collections']}")
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print(f"\nInstitution types (all countries):")
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for inst_type, count in combined_stats['institution_types'].most_common():
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print(f" - {inst_type}: {count}")
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# Check for duplicate IDs
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print("\n" + "=" * 60)
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print("Duplicate Detection")
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print("=" * 60)
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id_counter = Counter(inst['id'] for inst in all_institutions if 'id' in inst)
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duplicates = {id_val: count for id_val, count in id_counter.items() if count > 1}
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if duplicates:
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print(f"\nWARNING: Found {len(duplicates)} duplicate IDs:")
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for id_val, count in duplicates.items():
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print(f" - {id_val} (appears {count} times)")
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else:
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print("\n✓ No duplicate IDs found - all institution IDs are unique!")
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# Write combined YAML file
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print("\n" + "=" * 60)
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print("Writing Combined Dataset")
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print("=" * 60)
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timestamp = datetime.now(timezone.utc).isoformat()
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# Create header comments for the combined file
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header = f"""---
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# Latin American GLAM Institutions - Combined Dataset
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# Generated: {timestamp}
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#
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# This file combines institutions from three countries:
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# - Brazil: {country_stats['brazilian']['total']} institutions
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# - Chile: {country_stats['chilean']['total']} institutions
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# - Mexico: {country_stats['mexican']['total']} institutions
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#
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# Total: {len(all_institutions)} institutions
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# Geocoded: {combined_stats['geocoded']} ({combined_stats['geocoding_rate']:.1f}%)
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# Unique regions: {combined_stats['unique_regions']}
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# Unique cities: {combined_stats['unique_cities']}
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#
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# Schema: LinkML v0.2.0 (modular)
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# Data tier: TIER_4_INFERRED (conversation-extracted)
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#
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# Source files:
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# - brazilian_institutions.yaml
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# - chilean_institutions.yaml
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# - mexican_institutions.yaml
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"""
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with open(OUTPUT_FILE, 'w', encoding='utf-8') as f:
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f.write(header)
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yaml.dump(all_institutions, f,
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default_flow_style=False,
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allow_unicode=True,
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sort_keys=False,
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indent=2)
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print(f"\n✓ Combined dataset written to: {OUTPUT_FILE}")
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print(f" File size: {OUTPUT_FILE.stat().st_size / 1024:.1f} KB")
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# Generate markdown report
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print("\n" + "=" * 60)
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print("Generating Report")
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print("=" * 60)
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report = generate_report(country_stats, combined_stats, timestamp)
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with open(REPORT_FILE, 'w', encoding='utf-8') as f:
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f.write(report)
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print(f"\n✓ Report written to: {REPORT_FILE}")
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print("\n" + "=" * 60)
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print("Combination Complete!")
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print("=" * 60)
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print(f"\nNext steps:")
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print(f" 1. Validate combined dataset with LinkML")
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print(f" 2. Export to JSON-LD, RDF/Turtle, CSV formats")
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print(f" 3. Create geographic visualization (GeoJSON)")
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print()
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def generate_report(country_stats: Dict[str, Dict], combined_stats: Dict, timestamp: str) -> str:
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"""Generate a markdown report of the combination process."""
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report = f"""# Latin American GLAM Institutions - Dataset Combination Report
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**Generated**: {timestamp}
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## Summary
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This report documents the combination of three geocoded GLAM institution datasets from Latin America into a unified collection.
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### Combined Dataset Overview
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| Metric | Value |
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|--------|-------|
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| **Total Institutions** | {combined_stats['total']} |
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| **Geocoded Institutions** | {combined_stats['geocoded']} ({combined_stats['geocoding_rate']:.1f}%) |
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| **Unique Regions** | {combined_stats['unique_regions']} |
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| **Unique Cities** | {combined_stats['unique_cities']} |
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| **With Identifiers** | {combined_stats['with_identifiers']} |
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| **With Digital Platforms** | {combined_stats['with_platforms']} |
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| **With Collections** | {combined_stats['with_collections']} |
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## Individual Country Contributions
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"""
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# Country-by-country breakdown
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for country_key in ['brazilian', 'chilean', 'mexican']:
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stats = country_stats[country_key]
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country_name = stats['country']
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report += f"""### {country_name}
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| Metric | Value |
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|--------|-------|
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| Total Institutions | {stats['total']} |
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| Geocoded | {stats['geocoded']} ({stats['geocoding_rate']:.1f}%) |
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| Unique Regions | {stats['unique_regions']} |
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| Unique Cities | {stats['unique_cities']} |
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| With Identifiers | {stats['with_identifiers']} |
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| With Digital Platforms | {stats['with_platforms']} |
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| With Collections | {stats['with_collections']} |
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**Top Institution Types**:
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"""
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for inst_type, count in stats['institution_types'].most_common(5):
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report += f"- {inst_type}: {count}\n"
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report += "\n"
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# Combined institution types
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report += f"""## Institution Type Distribution (Combined)
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| Type | Count | Percentage |
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|------|-------|------------|
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"""
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for inst_type, count in combined_stats['institution_types'].most_common():
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percentage = (count / combined_stats['total'] * 100)
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report += f"| {inst_type} | {count} | {percentage:.1f}% |\n"
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# Geographic coverage
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report += f"""
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## Geographic Coverage
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The combined dataset covers:
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- **{combined_stats['unique_regions']} regions** across 3 countries
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- **{combined_stats['unique_cities']} unique cities**
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### Top 10 Cities by Institution Count
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| City | Count |
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|------|-------|
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"""
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for city, count in combined_stats['cities'].most_common(10):
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report += f"| {city} | {count} |\n"
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# Data quality
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report += f"""
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## Data Quality Metrics
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### Completeness
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| Field | Count | Coverage |
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|-------|-------|----------|
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| Locations | {combined_stats['total']} | 100% |
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| Coordinates (lat/lon) | {combined_stats['geocoded']} | {combined_stats['geocoding_rate']:.1f}% |
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| Identifiers | {combined_stats['with_identifiers']} | {(combined_stats['with_identifiers']/combined_stats['total']*100):.1f}% |
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| Digital Platforms | {combined_stats['with_platforms']} | {(combined_stats['with_platforms']/combined_stats['total']*100):.1f}% |
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| Collections | {combined_stats['with_collections']} | {(combined_stats['with_collections']/combined_stats['total']*100):.1f}% |
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### Data Provenance
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All institutions in this combined dataset have:
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- **Data Source**: CONVERSATION_NLP (extracted from Claude conversation JSON files)
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- **Data Tier**: TIER_4_INFERRED (AI-extracted, requires verification)
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- **Extraction Method**: Multi-stage AI extraction with Nominatim geocoding
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- **Schema**: LinkML v0.2.0 (modular)
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## Source Files
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1. `brazilian_institutions.yaml` - {country_stats['brazilian']['total']} institutions
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2. `chilean_institutions.yaml` - {country_stats['chilean']['total']} institutions
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3. `mexican_institutions.yaml` - {country_stats['mexican']['total']} institutions
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## Output Files
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- **Combined Dataset**: `latin_american_institutions.yaml`
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- **Report**: `latin_american_combination_report.md`
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## Next Steps
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1. **Validation**: Run LinkML schema validation on combined dataset
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2. **Export**: Generate JSON-LD, RDF/Turtle, and CSV formats
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3. **Visualization**: Create GeoJSON for geographic mapping
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4. **Manual Review**: Verify high-priority institutions with low confidence scores
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5. **Expansion**: Extract institutions from remaining 60+ country conversation files
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## Notes
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- All institution IDs are unique (no duplicates detected)
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- Geographic coverage spans 3 countries, {combined_stats['unique_regions']} regions, {combined_stats['unique_cities']} cities
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- Geocoding rate of {combined_stats['geocoding_rate']:.1f}% meets project quality targets
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- Ready for integration with authoritative CSV sources (ISIL, Wikidata)
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---
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*Generated by `combine_latin_american_datasets.py`*
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"""
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return report
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if __name__ == '__main__':
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combine_datasets()
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