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.
486 lines
19 KiB
Python
Executable file
486 lines
19 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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Wikidata enrichment for Tunisian heritage institutions with entity type and geographic validation.
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This version adds TWO layers of semantic validation to eliminate false positives:
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- Verifies entity type matches institution type (museums should be museums, not banks)
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- Verifies geographic location for location-specific institutions (universities, research centers)
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- Uses fuzzy matching for multilingual name variations
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- Prevents matches like "Banque de Tunisie", "lac de Tunis", or wrong-city universities
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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, Set
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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/3.0"
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# Institution type mapping: LinkML enum -> Wikidata entity types
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INSTITUTION_TYPE_MAPPING = {
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'MUSEUM': {
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'Q33506', # Museum
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'Q1030034', # Archaeological museum
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'Q3329412', # Archaeological museum (variant - Tunisia specific)
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'Q473972', # Art museum
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'Q2668072', # National museum
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'Q207694', # History museum
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'Q7328910', # Science museum
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'Q15243387', # Cultural heritage site
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'Q3152824', # Archaeological site (for heritage museums)
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'Q1153562', # Open-air museum
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'Q1496967', # Folk museum
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'Q17431399', # Heritage museum
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'Q28835878', # Heritage site
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},
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'LIBRARY': {
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'Q7075', # Library
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'Q2668072', # National library
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'Q570116', # Public library
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'Q5193377', # University library
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'Q28564', # Academic library
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'Q1479716', # Regional library
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'Q1622062', # Digital library
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'Q17297735', # Diocesan library
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'Q105338594', # Bibliothèque diocésaine (specific diocesan library subtype)
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},
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'ARCHIVE': {
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'Q166118', # Archive
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'Q7840289', # Art gallery (can have archival collections)
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'Q2668072', # National archive
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'Q1497375', # Historical archive
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'Q64578911', # Regional archive
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},
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'HOLY_SITES': {
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'Q22687', # Synagogue
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'Q16970', # Church
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'Q32815', # Mosque
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'Q44539', # Temple
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'Q44613', # Monastery
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'Q34627', # Synagogue (duplicate but included for safety)
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'Q697295', # Cathedral
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'Q56242275', # Pilgrimage site
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},
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'GALLERY': {
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'Q7840289', # Art gallery
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'Q473972', # Art museum
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'Q1007870', # Art centre
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},
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'UNIVERSITY': {
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'Q3918', # University
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'Q875538', # Public university
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'Q2467461', # Private university
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'Q15936437', # Research university
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'Q38723', # Higher education institution
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'Q3354859', # Technical university
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},
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'RESEARCH_CENTER': {
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'Q31855', # Research institute
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'Q7315155', # Research center
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'Q2467461', # Research institution
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'Q483242', # Laboratory
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'Q1664720', # Institute
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},
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'EDUCATION_PROVIDER': {
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'Q2385804', # Educational institution
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'Q5341295', # Music school
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'Q1664720', # Institute
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'Q180958', # Faculty
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'Q38723', # Higher education institution
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},
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'OFFICIAL_INSTITUTION': {
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'Q7210356', # Cultural institution
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'Q7840289', # Cultural center
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'Q1030034', # Cultural heritage institution
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'Q1664720', # Institute
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'Q7210356', # Government cultural organization
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},
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'PERSONAL_COLLECTION': {
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'Q7075', # Library (personal libraries are still libraries)
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'Q166118', # Archive (personal archives)
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'Q33506', # Museum (personal museums)
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},
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'MIXED': {
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'Q33506', # Museum
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'Q7075', # Library
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'Q166118', # Archive
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'Q7210356', # Cultural institution
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'Q7840289', # Cultural center
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'Q1030034', # Cultural complex
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}
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}
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def get_valid_types_for_institution(inst_type: str) -> Set[str]:
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"""Get set of valid Wikidata entity types for institution type."""
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return INSTITUTION_TYPE_MAPPING.get(inst_type, set())
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def search_wikidata_with_validation(
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name: str,
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inst_type: str,
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city: Optional[str] = None,
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alternative_names: Optional[List[str]] = None,
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timeout: int = 60
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) -> Optional[Dict[str, Any]]:
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"""
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Search Wikidata for Tunisian heritage institutions with entity type and geographic validation.
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Args:
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name: Institution name to search
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inst_type: Institution type (MUSEUM, LIBRARY, ARCHIVE, etc.)
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city: Optional city name for additional filtering
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alternative_names: List of alternative names to try if primary name fails
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timeout: Query timeout in seconds
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Returns:
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Dict with Wikidata data if valid match found, None otherwise
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"""
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# Get valid Wikidata entity types for this institution type
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valid_types = get_valid_types_for_institution(inst_type)
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if not valid_types:
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print(f" ⚠️ Unknown institution type: {inst_type}")
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return None
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# Build VALUES clause for SPARQL query - filter by institution type server-side
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# This ensures we get relevant results within the 200-result LIMIT
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type_values = " ".join([f"wd:{qid}" for qid in valid_types])
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# Build SPARQL query - fetch Tunisian institutions matching the specific type
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query = f"""
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SELECT DISTINCT ?item ?itemLabel ?itemDescription ?type ?typeLabel
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?viaf ?isil ?website ?coords ?inception ?itemAltLabel
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?location ?locationLabel
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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 have an instance-of type matching our institution type
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?item wdt:P31 ?type .
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# Filter to relevant types for this institution (server-side filtering)
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VALUES ?type {{ {type_values} }}
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# Add location (P131: located in administrative territorial entity)
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OPTIONAL {{ ?item wdt:P131 ?location . }}
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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 200
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"""
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headers = {'User-Agent': USER_AGENT}
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params = {
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'query': query,
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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 results WITH entity type AND geographic validation
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best_match = None
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best_score = 0
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matched_name = name # Track which name produced the match
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# Prepare all names to try (primary + alternatives)
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names_to_try = [name]
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if alternative_names:
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names_to_try.extend(alternative_names)
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city_lower = city.lower() if city else None
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# Location-specific institution types require stricter geographic matching
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requires_city_match = inst_type in {'UNIVERSITY', 'RESEARCH_CENTER', 'EDUCATION_PROVIDER'}
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# Try each name variation
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for name_variant in names_to_try:
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name_lower = name_variant.lower()
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for binding in bindings:
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# CRITICAL: Validate entity type FIRST
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entity_type_uri = binding.get("type", {}).get("value", "")
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entity_type_qid = entity_type_uri.split("/")[-1] if entity_type_uri else None
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# Skip if entity type doesn't match our institution type
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if entity_type_qid not in valid_types:
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continue
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# GEOGRAPHIC VALIDATION: Check location match for location-specific institutions
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if city_lower and requires_city_match:
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location_label = binding.get("locationLabel", {}).get("value", "").lower() if binding.get("locationLabel") else ""
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# Must have location data
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if not location_label:
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continue
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# Location must match expected city (fuzzy match for spelling variations)
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location_match = fuzz.ratio(city_lower, location_label)
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if location_match < 70: # Location mismatch - skip this result
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continue
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# Now do fuzzy matching on validated entities only
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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 using multiple strategies
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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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matched_name = name_variant # Record which name variation matched
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# Require minimum 65% match (lowered to capture multilingual variations)
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if best_score < 65:
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return None
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# Extract data from best match
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if not best_match:
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return None
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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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"entity_type": best_match.get("typeLabel", {}).get("value", ""),
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"match_score": best_score,
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"matched_name": matched_name # Record which name variant matched
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}
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# Add optional fields if present
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viaf_data = best_match.get("viaf")
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if viaf_data and isinstance(viaf_data, dict):
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result["viaf"] = viaf_data.get("value", "")
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isil_data = best_match.get("isil")
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if isil_data and isinstance(isil_data, dict):
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result["isil"] = isil_data.get("value", "")
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website_data = best_match.get("website")
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if website_data and isinstance(website_data, dict):
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result["website"] = website_data.get("value", "")
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inception_data = best_match.get("inception")
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if inception_data and isinstance(inception_data, dict):
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result["founded_date"] = inception_data.get("value", "").split("T")[0]
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coords_data = best_match.get("coords")
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if coords_data and isinstance(coords_data, dict):
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coords_str = coords_data.get("value", "")
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if coords_str and 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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'identifier_url': f"https://isil.org/{wikidata_result['isil']}"
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})
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# Update provenance notes with entity type validation
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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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entity_type = wikidata_result.get('entity_type', 'unknown')
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enrich_note = f" Wikidata enriched {datetime.now(timezone.utc).strftime('%Y-%m-%d')} ({wikidata_result['qid']} [{entity_type}], match: {wikidata_result.get('match_score', 0):.0f}%, validated)."
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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 (validated)' not in data['_metadata'].get('enhancements', []):
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data['_metadata']['enhancements'].append('Wikidata enrichment (validated)')
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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 (Entity Type + Geographic Validated)")
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print("=" * 60)
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print("Features:")
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print(" ✅ Entity type validation (museums must be museums, not banks)")
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print(" ✅ Geographic validation (universities must be in correct city)")
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print(" ✅ Fuzzy name matching (70% threshold)")
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print(" ✅ Multilingual support (French, Arabic, English)")
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print(" ✅ Checkpoint saving every 10 institutions")
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print(" ✅ Prevents false positives (Banque, lac, wrong-city matches)")
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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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'type_mismatch': 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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inst_type = inst.get('institution_type', 'MIXED')
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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 type validation
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print(f"[{i}/{len(institutions)}] Searching: {name} [{inst_type}] ({city})")
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stats['searched'] += 1
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# Extract alternative names for multilingual matching
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alt_names = inst.get('alternative_names', [])
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result = search_wikidata_with_validation(name, inst_type, city, alternative_names=alt_names, 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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entity_type = result.get('entity_type', 'unknown')
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matched_name = result.get('matched_name', name)
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# Show which name variant was used for matching
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name_note = f" [matched: {matched_name}]" if matched_name != name else ""
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print(f" ✅ Found: {result['qid']} [{entity_type}] - {result.get('name', '')} (match: {match_score:.0f}%{name_note})")
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# Accept matches above 70% (already validated by entity type)
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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 with validated match")
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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 or type mismatch")
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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 (VALIDATED)")
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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']}")
|
|
print(f"Found (validated): {stats['found']}")
|
|
print(f"Enriched (new): {stats['enriched']}")
|
|
print(f"Failed: {stats['failed']}")
|
|
print(f" - Type mismatches filtered: {stats['type_mismatch']}")
|
|
print(f" - Low confidence: {stats['low_confidence']}")
|
|
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:
|
|
improvement = stats['enriched']
|
|
print(f"✨ Added {improvement} new validated Wikidata identifiers!")
|
|
print(f"✅ All matches validated against correct entity types")
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|
|
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print("\n✅ Wikidata enrichment complete!")
|
|
|
|
if __name__ == '__main__':
|
|
main()
|