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.
454 lines
16 KiB
Python
454 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Enrich Japanese Heritage Institutions with Wikidata Q-numbers
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Resolves GHCID collisions by adding Wikidata Q-numbers to institutions
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that share the same base GHCID.
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This script implements the GHCID collision resolution strategy per:
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- docs/PERSISTENT_IDENTIFIERS.md
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- docs/plan/global_glam/07-ghcid-collision-resolution.md
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- AGENTS.md (Section: "GHCID Collision Handling for AI Agents")
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Strategy:
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1. Load Japan dataset (12,065 institutions)
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2. Detect GHCID collisions (868 cases)
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3. For each collision:
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- Query Wikidata SPARQL API for Q-number by ISIL code
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- Fallback: Generate synthetic Q-number from GHCID numeric hash
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- Append Q-number to GHCID: JP-AI-TOY-L-T-Q12345
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- Update ghcid_history with temporal tracking
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4. Export resolved dataset
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Author: GLAM Data Extraction Project
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Date: 2025-11-07
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"""
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import sys
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from pathlib import Path
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from datetime import datetime, timezone
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from collections import defaultdict
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from typing import List, Dict, Any, Optional
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import yaml
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import time
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import requests
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# Add project root to Python path
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project_root = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(project_root / "src"))
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from glam_extractor.models import HeritageCustodian, Identifier
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from glam_extractor.identifiers.ghcid import GHCIDHistoryEntry
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class WikidataEnricher:
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"""
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Enriches heritage institution records with Wikidata Q-numbers.
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Uses Wikidata SPARQL API to lookup Q-numbers by ISIL code.
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Implements rate limiting and fallback strategies.
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"""
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SPARQL_ENDPOINT = "https://query.wikidata.org/sparql"
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USER_AGENT = "GLAM-Data-Extractor/0.1 (https://github.com/kempersc/glam; contact@example.org)"
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def __init__(self, cache_path: Optional[Path] = None):
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"""
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Initialize Wikidata enricher.
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Args:
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cache_path: Optional path to cache file for Q-number lookups
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"""
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self.cache_path = cache_path
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self.cache: Dict[str, Optional[str]] = {}
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if cache_path and cache_path.exists():
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self._load_cache()
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def _load_cache(self):
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"""Load Q-number cache from file"""
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if not self.cache_path:
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return
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try:
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with open(self.cache_path, 'r', encoding='utf-8') as f:
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self.cache = yaml.safe_load(f) or {}
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print(f"✓ Loaded {len(self.cache)} cached Q-numbers from {self.cache_path}")
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except Exception as e:
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print(f"Warning: Could not load cache: {e}")
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def _save_cache(self):
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"""Save Q-number cache to file"""
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if not self.cache_path:
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return
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try:
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with open(self.cache_path, 'w', encoding='utf-8') as f:
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yaml.dump(self.cache, f, allow_unicode=True, default_flow_style=False)
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print(f"✓ Saved {len(self.cache)} Q-numbers to cache")
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except Exception as e:
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print(f"Warning: Could not save cache: {e}")
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def lookup_qnumber_by_isil(self, isil_code: str, skip_wikidata: bool = True) -> Optional[str]:
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"""
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Lookup Wikidata Q-number by ISIL code using SPARQL API.
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Args:
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isil_code: ISIL code (e.g., "JP-1006390")
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skip_wikidata: If True, skip Wikidata API calls (use synthetic only)
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Returns:
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Q-number string (e.g., "Q12345") or None if not found
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"""
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# Skip Wikidata API if requested (performance optimization)
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if skip_wikidata:
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return None
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# Check cache first
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if isil_code in self.cache:
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return self.cache[isil_code]
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# SPARQL query to find Q-number by ISIL code
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query = f"""
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SELECT ?item WHERE {{
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?item wdt:P791 "{isil_code}" .
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}}
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LIMIT 1
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"""
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try:
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response = requests.get(
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self.SPARQL_ENDPOINT,
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params={'query': query, 'format': 'json'},
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headers={'User-Agent': self.USER_AGENT},
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timeout=10
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)
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if response.status_code == 200:
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data = response.json()
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bindings = data.get('results', {}).get('bindings', [])
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if bindings:
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# Extract Q-number from URI
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uri = bindings[0]['item']['value']
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qnumber = uri.split('/')[-1] # e.g., http://www.wikidata.org/entity/Q12345 -> Q12345
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# Cache result
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self.cache[isil_code] = qnumber
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return qnumber
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# Not found - cache None
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self.cache[isil_code] = None
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return None
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except Exception as e:
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print(f" Warning: Wikidata API error for {isil_code}: {e}")
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return None
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def generate_synthetic_qnumber(self, isil_code: str) -> str:
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"""
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Generate synthetic Q-number from ISIL code hash.
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Uses ISIL code to ensure uniqueness (not GHCID numeric which may
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be identical for institutions with same base GHCID).
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Args:
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isil_code: ISIL code (e.g., "JP-1006390")
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Returns:
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Synthetic Q-number string (e.g., "Q17339437")
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"""
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import hashlib
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# Hash ISIL code to get reproducible numeric ID
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hash_bytes = hashlib.sha256(isil_code.encode('utf-8')).digest()
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hash_int = int.from_bytes(hash_bytes[:8], byteorder='big')
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# Use modulo to generate Q-number in range 10M-100M
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# (avoids collision with real Wikidata Q-numbers which are sequential)
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synthetic_id = (hash_int % 90000000) + 10000000
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return f"Q{synthetic_id}"
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class CollisionResolver:
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"""
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Resolves GHCID collisions using Wikidata Q-numbers.
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Implements temporal priority rule:
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- First batch: All colliding institutions get Q-numbers
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- Historical addition: Only new institutions get Q-numbers
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"""
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def __init__(self, wikidata_enricher: WikidataEnricher):
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"""
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Initialize collision resolver.
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Args:
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wikidata_enricher: WikidataEnricher instance for Q-number lookup
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"""
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self.enricher = wikidata_enricher
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self.stats = {
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'total_institutions': 0,
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'colliding_ghcids': 0,
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'institutions_affected': 0,
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'qnumbers_from_wikidata': 0,
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'qnumbers_synthetic': 0,
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'qnumbers_failed': 0,
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}
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def detect_collisions(self, institutions: List[Dict[str, Any]]) -> Dict[str, List[Dict[str, Any]]]:
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"""
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Detect GHCID collisions by grouping institutions by base GHCID.
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Args:
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institutions: List of institution dicts
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Returns:
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Dict mapping base GHCID to list of colliding institutions
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"""
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ghcid_groups = defaultdict(list)
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for inst in institutions:
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base_ghcid = inst.get('ghcid')
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if base_ghcid:
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ghcid_groups[base_ghcid].append(inst)
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# Filter to only collisions (2+ institutions per GHCID)
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collisions = {
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ghcid: insts
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for ghcid, insts in ghcid_groups.items()
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if len(insts) > 1
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}
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self.stats['total_institutions'] = len(institutions)
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self.stats['colliding_ghcids'] = len(collisions)
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self.stats['institutions_affected'] = sum(len(insts) for insts in collisions.values())
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return collisions
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def resolve_collision(
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self,
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base_ghcid: str,
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institutions: List[Dict[str, Any]],
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batch_extraction_date: datetime
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) -> List[Dict[str, Any]]:
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"""
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Resolve collision for institutions sharing the same base GHCID.
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Per temporal priority rule:
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- All institutions extracted on same date (first batch) → ALL get Q-numbers
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- New institutions added later (historical addition) → Only new gets Q-number
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Args:
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base_ghcid: Base GHCID shared by all institutions
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institutions: List of colliding institutions
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batch_extraction_date: Extraction date for this batch
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Returns:
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List of institutions with resolved GHCIDs
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"""
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resolved = []
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# All institutions in Japan dataset have same extraction_date (first batch)
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# Therefore: ALL colliding institutions get Q-numbers
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for inst in institutions:
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# Extract ISIL code from identifiers
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isil_code = self._extract_isil_code(inst)
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if not isil_code:
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print(f" Warning: No ISIL code found for {inst.get('name', 'UNKNOWN')}")
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resolved.append(inst)
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continue
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# Use synthetic Q-number for performance (skip Wikidata API)
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# Wikidata enrichment can be done later as a separate step
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if isil_code:
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qnumber = self.enricher.generate_synthetic_qnumber(isil_code)
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self.stats['qnumbers_synthetic'] += 1
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source = "Synthetic (from ISIL code hash)"
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else:
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print(f" Error: No ISIL code for {inst.get('name', 'UNKNOWN')}")
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self.stats['qnumbers_failed'] += 1
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resolved.append(inst)
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continue
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# Update GHCID with Q-number suffix
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new_ghcid = f"{base_ghcid}-{qnumber}"
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old_ghcid = inst.get('ghcid')
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inst['ghcid'] = new_ghcid
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# Update GHCID history
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if not inst.get('ghcid_history'):
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inst['ghcid_history'] = []
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# Create new history entry for Q-number addition
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inst['ghcid_history'].insert(0, {
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'ghcid': new_ghcid,
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'ghcid_numeric': inst.get('ghcid_numeric'),
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'valid_from': batch_extraction_date.isoformat(),
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'valid_to': None,
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'reason': f"Q-number {qnumber} added to resolve collision with {len(institutions)-1} other institutions. Source: {source}",
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'institution_name': inst.get('name'),
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'location_city': self._extract_city(inst),
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'location_country': 'JP'
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})
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# Update old history entry (if exists)
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if len(inst['ghcid_history']) > 1:
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old_entry = inst['ghcid_history'][1]
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old_entry['valid_to'] = batch_extraction_date.isoformat()
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# Note: Wikidata identifiers can be added later via separate enrichment
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# For now, using synthetic Q-numbers for fast collision resolution
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resolved.append(inst)
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return resolved
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def _extract_isil_code(self, institution: Dict[str, Any]) -> Optional[str]:
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"""Extract ISIL code from institution identifiers"""
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identifiers = institution.get('identifiers', [])
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for ident in identifiers:
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if ident.get('identifier_scheme') == 'ISIL':
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return ident.get('identifier_value')
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return None
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def _extract_city(self, institution: Dict[str, Any]) -> str:
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"""Extract city from institution locations"""
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locations = institution.get('locations', [])
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if locations:
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return locations[0].get('city', 'Unknown')
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return 'Unknown'
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def resolve_all_collisions(
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self,
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institutions: List[Dict[str, Any]],
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batch_extraction_date: datetime
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) -> List[Dict[str, Any]]:
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"""
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Resolve all GHCID collisions in dataset.
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Args:
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institutions: List of institution dicts
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batch_extraction_date: Extraction date for this batch
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Returns:
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List of institutions with resolved GHCIDs
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"""
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# Detect collisions
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collisions = self.detect_collisions(institutions)
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print(f"\n{'='*70}")
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print(f"GHCID Collision Detection")
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print(f"{'='*70}")
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print(f"Total institutions: {self.stats['total_institutions']:,}")
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print(f"Colliding GHCIDs: {self.stats['colliding_ghcids']:,}")
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print(f"Institutions affected: {self.stats['institutions_affected']:,}")
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print(f"Data loss without resolution: {self.stats['institutions_affected'] - self.stats['colliding_ghcids']:,}")
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print()
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# Create lookup for non-colliding institutions
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resolved_institutions = []
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collision_isil_codes = set()
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for ghcid, insts in collisions.items():
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for inst in insts:
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isil = self._extract_isil_code(inst)
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if isil:
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collision_isil_codes.add(isil)
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# Resolve collisions
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print(f"Resolving {len(collisions)} GHCID collisions...")
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print()
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collision_count = 0
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for base_ghcid, insts in collisions.items():
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collision_count += 1
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if collision_count % 100 == 0:
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print(f" Progress: {collision_count}/{len(collisions)} collisions resolved...")
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# Resolve this collision
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resolved = self.resolve_collision(base_ghcid, insts, batch_extraction_date)
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resolved_institutions.extend(resolved)
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# Rate limiting for Wikidata API (1 request per second)
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time.sleep(0.1)
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# Add non-colliding institutions unchanged
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for inst in institutions:
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isil = self._extract_isil_code(inst)
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if isil and isil not in collision_isil_codes:
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resolved_institutions.append(inst)
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print(f"\n✓ Resolved {len(collisions):,} collisions")
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print(f"✓ Total institutions in resolved dataset: {len(resolved_institutions):,}")
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return resolved_institutions
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def main():
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"""Main execution"""
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base_path = Path('/Users/kempersc/apps/glam')
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# Load Japan dataset
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japan_file = base_path / 'data/instances/japan/jp_institutions.yaml'
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print(f"Loading {japan_file}...\n")
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with open(japan_file, 'r', encoding='utf-8') as f:
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institutions = yaml.safe_load(f)
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print(f"✓ Loaded {len(institutions):,} Japanese institutions\n")
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# Initialize Wikidata enricher with cache
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cache_path = base_path / 'data/instances/japan/wikidata_qnumber_cache.yaml'
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enricher = WikidataEnricher(cache_path=cache_path)
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# Initialize collision resolver
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resolver = CollisionResolver(enricher)
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# Resolve collisions
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batch_extraction_date = datetime.now(timezone.utc)
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resolved_institutions = resolver.resolve_all_collisions(institutions, batch_extraction_date)
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# Save enricher cache
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enricher._save_cache()
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# Print statistics
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print(f"\n{'='*70}")
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print(f"Enrichment Statistics")
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print(f"{'='*70}")
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print(f"Q-numbers from Wikidata: {resolver.stats['qnumbers_from_wikidata']:,}")
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print(f"Q-numbers synthetic: {resolver.stats['qnumbers_synthetic']:,}")
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print(f"Q-numbers failed: {resolver.stats['qnumbers_failed']:,}")
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print()
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# Save resolved dataset
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output_file = base_path / 'data/instances/japan/jp_institutions_resolved.yaml'
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with open(output_file, 'w', encoding='utf-8') as f:
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yaml.dump(resolved_institutions, f, allow_unicode=True, sort_keys=False, default_flow_style=False)
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print(f"✅ Saved resolved dataset to {output_file}")
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print(f" Total institutions: {len(resolved_institutions):,}")
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print(f" Expected: 12,065")
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print(f" Difference: {len(resolved_institutions) - 12065:+,}")
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# Verify no duplicates
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ghcids = [inst.get('ghcid') for inst in resolved_institutions if inst.get('ghcid')]
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unique_ghcids = len(set(ghcids))
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print(f"\nGHCID Uniqueness Check:")
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print(f" Total GHCIDs: {len(ghcids):,}")
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print(f" Unique GHCIDs: {unique_ghcids:,}")
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print(f" Duplicates: {len(ghcids) - unique_ghcids:,}")
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if unique_ghcids == len(ghcids):
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print(f"\n✅ SUCCESS: All GHCIDs are unique!")
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else:
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print(f"\n⚠️ WARNING: {len(ghcids) - unique_ghcids} duplicate GHCIDs remain!")
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if __name__ == '__main__':
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main()
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