305 lines
11 KiB
Python
Executable file
305 lines
11 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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Wikidata enrichment for Tunisian heritage institutions using fuzzy search.
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Searches Wikidata by CONTAINS search rather than exact label match,
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then uses fuzzy matching to verify results.
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GLAM Data Extraction Project
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Schema: LinkML v0.2.1
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"""
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import yaml
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import time
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import requests
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, Dict, Any, List
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from rapidfuzz import fuzz
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SPARQL_ENDPOINT = "https://query.wikidata.org/sparql"
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USER_AGENT = "GLAM-Tunisia-Wikidata-Enrichment/2.0"
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def search_wikidata_fuzzy(name: str, city: Optional[str] = None, timeout: int = 60) -> Optional[Dict[str, Any]]:
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"""
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Search Wikidata for Tunisian heritage institutions using broader criteria.
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Returns best fuzzy match from results.
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"""
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# Try multiple search strategies
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queries = []
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# Strategy 1: Search by country + institution type
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# Get all museums/libraries/archives in Tunisia, then fuzzy match client-side
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query1 = """
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SELECT DISTINCT ?item ?itemLabel ?itemDescription ?viaf ?isil ?website ?coords ?inception ?itemAltLabel
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WHERE {
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# Must be in Tunisia
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?item wdt:P17 wd:Q948 .
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# Must be heritage institution type
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?item wdt:P31 ?type .
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VALUES ?type {
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wd:Q33506 # Museum
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wd:Q7075 # Library
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wd:Q166118 # Archive
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wd:Q1030034 # Archaeological museum
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wd:Q473972 # Art museum
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wd:Q570116 # Public library
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wd:Q22687 # Synagogue
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wd:Q7840289 # Art gallery
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wd:Q2668072 # National library
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}
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OPTIONAL { ?item wdt:P214 ?viaf . }
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OPTIONAL { ?item wdt:P791 ?isil . }
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OPTIONAL { ?item wdt:P856 ?website . }
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OPTIONAL { ?item wdt:P625 ?coords . }
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OPTIONAL { ?item wdt:P571 ?inception . }
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OPTIONAL { ?item skos:altLabel ?itemAltLabel . FILTER(LANG(?itemAltLabel) IN ("fr", "ar", "en")) }
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SERVICE wikibase:label { bd:serviceParam wikibase:language "fr,ar,en" . }
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}
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LIMIT 100
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"""
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headers = {'User-Agent': USER_AGENT}
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params = {
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'query': query1,
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'format': 'json'
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}
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try:
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time.sleep(1.5) # Rate limiting
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response = requests.get(SPARQL_ENDPOINT, params=params, headers=headers, timeout=timeout)
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response.raise_for_status()
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results = response.json()
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bindings = results.get("results", {}).get("bindings", [])
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if not bindings:
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return None
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# Fuzzy match against all results
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best_match = None
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best_score = 0
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name_lower = name.lower()
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for binding in bindings:
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item_label = binding.get("itemLabel", {}).get("value", "").lower()
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item_desc = binding.get("itemDescription", {}).get("value", "").lower()
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# Calculate match score
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label_score = fuzz.ratio(name_lower, item_label)
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partial_score = fuzz.partial_ratio(name_lower, item_label)
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token_score = fuzz.token_set_ratio(name_lower, item_label)
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# Best of the three fuzzy match strategies
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score = max(label_score, partial_score, token_score)
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if score > best_score:
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best_score = score
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best_match = binding
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# Require minimum 70% match
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if best_score < 70:
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return None
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# Extract data from best match
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item_uri = best_match.get("item", {}).get("value", "")
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qid = item_uri.split("/")[-1] if item_uri else None
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if not qid or not qid.startswith("Q"):
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return None
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result = {
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"qid": qid,
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"name": best_match.get("itemLabel", {}).get("value", ""),
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"description": best_match.get("itemDescription", {}).get("value", ""),
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"match_score": best_score
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}
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if "viaf" in best_match:
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result["viaf"] = best_match["viaf"]["value"]
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if "isil" in best_match:
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result["isil"] = best_match["isil"]["value"]
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if "website" in best_match:
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result["website"] = best_match["website"]["value"]
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if "inception" in best_match:
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result["founded_date"] = best_match["inception"]["value"].split("T")[0]
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if "coords" in best_match:
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coords_str = best_match["coords"]["value"]
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if coords_str.startswith("Point("):
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lon, lat = coords_str[6:-1].split()
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result["latitude"] = float(lat)
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result["longitude"] = float(lon)
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return result
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except requests.exceptions.Timeout:
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print(f" ⏱️ Query timeout (>{timeout}s)")
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return None
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except requests.exceptions.RequestException as e:
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print(f" ❌ Network error: {e}")
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return None
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except Exception as e:
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print(f" ❌ Error: {e}")
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return None
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def add_wikidata_to_institution(institution: dict, wikidata_result: dict):
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"""Add Wikidata information to institution record."""
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# Add Wikidata identifier
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if 'identifiers' not in institution:
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institution['identifiers'] = []
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# Check if Wikidata already exists
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existing_schemes = {i.get('identifier_scheme') for i in institution['identifiers']}
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if 'Wikidata' not in existing_schemes:
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institution['identifiers'].append({
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'identifier_scheme': 'Wikidata',
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'identifier_value': wikidata_result['qid'],
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'identifier_url': f"https://www.wikidata.org/wiki/{wikidata_result['qid']}"
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})
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# Add VIAF if present
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if wikidata_result.get('viaf') and 'VIAF' not in existing_schemes:
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institution['identifiers'].append({
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'identifier_scheme': 'VIAF',
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'identifier_value': wikidata_result['viaf'],
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'identifier_url': f"https://viaf.org/viaf/{wikidata_result['viaf']}"
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})
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# Add ISIL if present
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if wikidata_result.get('isil') and 'ISIL' not in existing_schemes:
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institution['identifiers'].append({
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'identifier_scheme': 'ISIL',
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'identifier_value': wikidata_result['isil'],
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# ISIL codes don't have a universal URLisil']}"
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})
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# Update provenance notes
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if 'provenance' not in institution:
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institution['provenance'] = {}
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notes = institution['provenance'].get('notes', '')
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enrich_note = f" Wikidata enriched {datetime.now(timezone.utc).strftime('%Y-%m-%d')} ({wikidata_result['qid']}, match: {wikidata_result.get('match_score', 0):.0f}%)."
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institution['provenance']['notes'] = (notes + enrich_note).strip()
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def save_checkpoint(data: dict, input_file: Path, stats: dict):
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"""Save progress checkpoint."""
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print(f"\n💾 Saving checkpoint... (enriched: {stats['enriched']}, total coverage: {stats['already_enriched'] + stats['enriched']}/{stats['total']})")
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data['_metadata']['generated'] = datetime.now(timezone.utc).isoformat()
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if 'Wikidata enrichment' not in data['_metadata'].get('enhancements', []):
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data['_metadata']['enhancements'].append('Wikidata enrichment')
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with open(input_file, 'w', encoding='utf-8') as f:
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yaml.dump(data, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
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def main():
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input_file = Path('data/instances/tunisia/tunisian_institutions_enhanced.yaml')
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print("Tunisia Wikidata Enrichment (Fuzzy Search)")
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print("=" * 60)
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print("Features:")
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print(" - Broad SPARQL query (all Tunisian heritage institutions)")
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print(" - Client-side fuzzy matching (70% threshold)")
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print(" - Checkpoint saving every 10 institutions")
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print(" - Multiple match strategies (exact, partial, token)")
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print("=" * 60)
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# Load data
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print(f"\nReading: {input_file}")
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with open(input_file, 'r', encoding='utf-8') as f:
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data = yaml.safe_load(f)
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institutions = data['institutions']
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print(f"Total institutions: {len(institutions)}")
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# Statistics
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stats = {
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'total': len(institutions),
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'already_enriched': 0,
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'searched': 0,
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'found': 0,
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'enriched': 0,
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'failed': 0,
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'low_confidence': 0
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}
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# Process each institution
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checkpoint_interval = 10
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for i, inst in enumerate(institutions, 1):
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name = inst.get('name', '')
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city = inst.get('locations', [{}])[0].get('city', '') if inst.get('locations') else ''
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# Check if already has Wikidata
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identifiers = inst.get('identifiers', [])
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existing_schemes = {id.get('identifier_scheme') for id in identifiers}
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if 'Wikidata' in existing_schemes:
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stats['already_enriched'] += 1
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qid = next((id['identifier_value'] for id in identifiers if id.get('identifier_scheme') == 'Wikidata'), 'unknown')
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print(f"[{i}/{len(institutions)}] ✓ {name} (already has {qid})")
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continue
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# Search Wikidata with fuzzy matching
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print(f"[{i}/{len(institutions)}] Searching: {name} ({city})")
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stats['searched'] += 1
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result = search_wikidata_fuzzy(name, city, timeout=60)
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if result:
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stats['found'] += 1
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match_score = result.get('match_score', 0)
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print(f" ✅ Found: {result['qid']} - {result.get('name', '')} (match: {match_score:.0f}%)")
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# Accept matches above 70%
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if match_score >= 70:
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add_wikidata_to_institution(inst, result)
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stats['enriched'] += 1
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print(f" ✅ Enriched")
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else:
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stats['low_confidence'] += 1
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stats['failed'] += 1
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print(f" ⚠️ Match score too low, skipping")
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else:
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stats['failed'] += 1
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print(f" ❌ Not found")
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# Checkpoint every N institutions
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if i % checkpoint_interval == 0 or i == len(institutions):
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save_checkpoint(data, input_file, stats)
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# Final save
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save_checkpoint(data, input_file, stats)
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# Print statistics
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print("\n" + "=" * 60)
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print("WIKIDATA ENRICHMENT STATISTICS")
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print("=" * 60)
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print(f"Total institutions: {stats['total']}")
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print(f"Already enriched: {stats['already_enriched']}")
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print(f"Searched: {stats['searched']}")
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print(f"Found: {stats['found']}")
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print(f"Enriched (new): {stats['enriched']}")
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print(f"Failed: {stats['failed']}")
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print(f"Low confidence: {stats['low_confidence']}")
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print(f"\nFinal Wikidata coverage: {stats['already_enriched'] + stats['enriched']}/{stats['total']} ({100*(stats['already_enriched'] + stats['enriched'])/stats['total']:.1f}%)")
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if stats['enriched'] > 0:
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improvement = stats['enriched']
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print(f"✨ Added {improvement} new Wikidata identifiers!")
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print("\n✅ Wikidata enrichment complete!")
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if __name__ == '__main__':
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main()
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