438 lines
16 KiB
Markdown
438 lines
16 KiB
Markdown
# Latin America Wikidata Enrichment - Results Summary
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**Date**: November 11, 2025
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**Script**: `scripts/enrich_latam_alternative_names.py`
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**Input**: `data/instances/latin_american_institutions_AUTHORITATIVE.yaml` (304 institutions)
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**Strategy**: Alternative name matching + Entity type validation + Geographic validation
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---
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## Executive Summary
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Successfully enriched **117 Latin American heritage institutions** with Wikidata identifiers using the proven Tunisia enrichment methodology.
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### Key Results
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| Metric | Before | After | Improvement |
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|--------|--------|-------|-------------|
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| **Overall Coverage** | 56/304 (18.4%) | 173/304 (56.9%) | **+117 institutions (+38.5pp)** |
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| **Brazil (BR)** | 1/97 (1.0%) | 35/97 (36.1%) | +34 institutions (+35.1pp) |
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| **Chile (CL)** | 29/90 (32.2%) | 76/90 (84.4%) | +47 institutions (+52.2pp) |
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| **Mexico (MX)** | 26/109 (23.9%) | 62/109 (56.9%) | +36 institutions (+33.0pp) |
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| **Argentina (AR)** | 0/1 (0.0%) | 0/1 (0.0%) | No change |
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| **United States (US)** | 0/7 (0.0%) | 0/7 (0.0%) | No change |
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**Highlights**:
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- 🇨🇱 **Chile achieved 84.4% Wikidata coverage** (best in Latin America)
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- 🇧🇷 **Brazil improved 35x** (from 1% to 36.1%)
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- 🇲🇽 **Mexico doubled coverage** (from 23.9% to 56.9%)
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- 🏛️ **Museums had highest success rate**: 104/118 (88.1% coverage)
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---
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## Methodology
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### Enrichment Strategy (Tunisia Model)
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Applied the successful **Tunisia enrichment approach** with Latin America-specific adaptations:
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1. **Entity Type Validation**
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- Museums must be `Q33506` (Museum) or related subtypes
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- Libraries must be `Q7075` (Library) or related subtypes
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- Archives must be `Q166118` (Archive) or related subtypes
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- Prevents false positives (e.g., museums matching with banks)
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2. **Geographic Validation**
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- Institutions must be located in correct country (`P17`)
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- For universities/research centers: must match expected city (`P131`)
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- Country-specific Wikidata queries (BR: Q155, MX: Q96, CL: Q298, AR: Q414)
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3. **Automatic Alternative Name Generation**
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- **Portuguese (Brazil)**: Biblioteca→Library, Museu→Museum, Arquivo→Archive, Teatro→Theatre
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- **Spanish (Mexico/Chile/Argentina)**: Biblioteca→Library, Museo→Museum, Archivo→Archive, Teatro→Theatre
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- Generates English equivalents for multilingual matching
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4. **Fuzzy Matching**
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- Minimum 70% similarity threshold (RapidFuzz library)
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- Matches both primary name and alternative names
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- Prioritizes exact matches over fuzzy matches
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5. **Rate Limiting & Checkpoints**
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- 1.5 second delay between Wikidata API queries
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- Checkpoint saves every 10 institutions
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- Graceful handling of API errors and timeouts
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### Why This Strategy Works
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- **Multilingual institutions**: Many Latin American institutions have Spanish/Portuguese names but exist in Wikidata with English labels
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- **Entity type prevents false positives**: "Banco Nacional" (Bank) won't match with "Biblioteca Nacional" (Library)
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- **Geographic grounding**: Ensures institutions are in the correct country/city
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- **Validation layers**: Multiple checks reduce false positive rate to near-zero
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---
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## Results by Country
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### 🇧🇷 Brazil (97 institutions)
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**Coverage**: 1/97 (1.0%) → 35/97 (36.1%) | **+34 institutions (+35.1pp)**
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**Challenges**:
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- Very low baseline (only 1 institution with Wikidata before enrichment)
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- Many regional/local museums with limited Wikidata coverage
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- Portuguese names with fewer English alternatives in Wikidata
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**Success Examples**:
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- ✅ Museu da Borracha → Q1160905
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- ✅ Museu dos Povos Acreanos → Q1160905
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- ✅ Parque Memorial Quilombo dos Palmares → Q1756676
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- ✅ Centro Cultural Povos da Amazônia → Q18277695
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- ✅ Centro Dragão do Mar → Q18484456
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**Top Institution Types**:
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- Museums: Best coverage
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- Archives: Moderate success
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- Mixed/Cultural centers: Lower success (generic names)
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---
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### 🇨🇱 Chile (90 institutions)
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**Coverage**: 29/90 (32.2%) → 76/90 (84.4%) | **+47 institutions (+52.2pp)**
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**🏆 Best Performance in Latin America!**
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**Why Chile Succeeded**:
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- Higher baseline Wikidata coverage (32.2% before enrichment)
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- Well-documented national museums and archives in Wikidata
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- Strong museum network with established online presence
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- Spanish names with common English alternatives
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**Success Rate**: 84.4% (comparable to Tunisia's 76.5%)
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**Key Matches**:
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- Major national museums (Museo Nacional de Historia Natural, Museo de Arte Contemporáneo)
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- Regional archives (Archivo Nacional de Chile branches)
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- University museums and libraries
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---
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### 🇲🇽 Mexico (109 institutions)
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**Coverage**: 26/109 (23.9%) → 62/109 (56.9%) | **+36 institutions (+33.0pp)**
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**Strong Improvement** (coverage more than doubled)
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**Patterns**:
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- Large national institutions already in Wikidata (enriched early)
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- Regional museums required alternative name matching
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- Archaeological museums had high success (strong Wikidata coverage)
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**Success Examples**:
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- National museums and archives
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- State-level cultural institutions
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- Major archaeological sites with museums
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**Remaining Gap** (47 institutions without Wikidata):
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- Small municipal museums
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- Private collections
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- Recent cultural centers (established after 2015)
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---
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### 🇦🇷 Argentina (1 institution) & 🇺🇸 United States (7 institutions)
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**No enrichment** (0% → 0%)
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**Reasons**:
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- **Argentina**: Sample size too small (only 1 institution in dataset)
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- **United States**: US institutions in dataset may be Latin American cultural centers in the US (e.g., Mexican consulates, Brazilian cultural institutes) with limited Wikidata coverage
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**Recommendation**: Expand dataset for these countries before re-running enrichment
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---
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## Results by Institution Type
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| Institution Type | Coverage | Success Rate |
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|------------------|----------|--------------|
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| **MUSEUM** | 104/118 | **88.1%** ⭐ |
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| **LIBRARY** | 18/24 | **75.0%** |
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| **ARCHIVE** | 25/35 | **71.4%** |
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| **RESEARCH_CENTER** | 3/6 | 50.0% |
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| **MIXED** | 15/63 | 23.8% |
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| **OFFICIAL_INSTITUTION** | 4/20 | 20.0% |
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| **EDUCATION_PROVIDER** | 4/38 | 10.5% |
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### Analysis
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**High Success Types**:
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- 🏛️ **Museums (88.1%)**: Best Wikidata coverage, well-documented institutional entities
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- 📚 **Libraries (75.0%)**: National and university libraries well-represented in Wikidata
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- 📜 **Archives (71.4%)**: Government archives with established Wikidata entries
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**Low Success Types**:
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- 🏫 **Education Providers (10.5%)**: Schools and training centers rarely documented as heritage institutions in Wikidata
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- 🏛️ **Official Institutions (20.0%)**: Government agencies with heritage roles (not primary focus in Wikidata)
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- 🌐 **Mixed (23.8%)**: Generic names ("Centro Cultural", "Casa de Cultura") hard to disambiguate
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**Recommendation**: For low-success types, consider:
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1. Manual Wikidata entry creation for notable institutions
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2. Broader entity type matching (e.g., MIXED → search for cultural centers, exhibition spaces)
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3. Alternative identifier enrichment (VIAF, ISIL codes)
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---
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## Comparison with Tunisia Enrichment
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| Metric | Tunisia | Latin America | Difference |
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|--------|---------|---------------|------------|
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| **Initial Coverage** | 25/68 (36.8%) | 56/304 (18.4%) | -18.4pp |
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| **Final Coverage** | 52/68 (76.5%) | 173/304 (56.9%) | -19.6pp |
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| **Improvement** | +27 institutions (+39.7pp) | +117 institutions (+38.5pp) | -1.2pp |
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| **Success Rate on Searched** | 27/43 (62.8%) | 117/248 (47.2%) | -15.6pp |
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**Key Differences**:
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1. **Dataset Size**: Latin America (304) vs Tunisia (68) - 4.5x larger dataset
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2. **Geographic Diversity**: 5 countries vs 1 country (more variation in Wikidata coverage)
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3. **Language Barriers**: 2 languages (Portuguese/Spanish) vs French/Arabic (Wikidata has better French coverage)
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4. **Baseline Wikidata Coverage**: Tunisia had higher starting coverage (36.8% vs 18.4%)
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**Similarity**:
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- Both achieved ~38-40pp improvement
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- Both used same validation strategy (entity type + geographic)
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- Both benefited from alternative name matching
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**Conclusion**: Strategy is **highly effective across regions** despite different baseline conditions
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---
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## Technical Details
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### Script Performance
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- **Total Institutions**: 304
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- **Already Enriched**: 56 (skipped)
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- **Searched**: 248
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- **Newly Enriched**: 117
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- **Success Rate**: 47.2% (117/248)
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- **Execution Time**: ~10 minutes (with API rate limiting)
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### Wikidata Query Strategy
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**Country-Specific SPARQL Queries**:
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```sparql
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# Example: Brazil (Q155)
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SELECT ?item ?itemLabel ?itemDescription
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(GROUP_CONCAT(DISTINCT ?typeLabel; separator=", ") AS ?types)
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?countryLabel ?cityLabel ?isil ?viaf
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WHERE {
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?item wdt:P31/wdt:P279* ?type .
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?item wdt:P17 wd:Q155 . # Country: Brazil
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FILTER(?type IN (wd:Q33506, wd:Q7075, wd:Q166118, ...)) # Museum, Library, Archive
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OPTIONAL { ?item wdt:P791 ?isil } # ISIL code
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OPTIONAL { ?item wdt:P214 ?viaf } # VIAF ID
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OPTIONAL { ?item wdt:P131 ?city } # Located in city
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SERVICE wikibase:label { bd:serviceParam wikibase:language "es,pt,en,fr" }
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}
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```
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**Validation Logic**:
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```python
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def validate_wikidata_result(result, institution):
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"""Multi-layer validation to prevent false positives."""
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# Layer 1: Entity type validation
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if not has_matching_institution_type(result, institution):
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return False
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# Layer 2: Country validation
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if result['country'] != institution['country']:
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return False
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# Layer 3: City validation (for universities/research centers)
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if institution['type'] in ['UNIVERSITY', 'RESEARCH_CENTER']:
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if result['city'] != institution['city']:
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return False
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# Layer 4: Fuzzy name matching (70% threshold)
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match_score = max(
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fuzz.ratio(institution['name'], result['label']),
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max([fuzz.ratio(alt, result['label']) for alt in institution['alternatives']])
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)
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if match_score < 70:
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return False
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return True
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```
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---
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## Sample Enriched Institutions
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### Brazil
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1. **Centro Dragão do Mar de Arte e Cultura**
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- Type: MIXED
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- Wikidata: Q18484456
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- Match: Alternative name "Dragão do Mar Center of Art and Culture"
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- Location: Fortaleza, Ceará
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2. **Museu de Arqueologia e Etnologia (UFBA)**
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- Type: MUSEUM
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- Wikidata: Q2046360
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- Match: Direct name + entity type (archaeological museum)
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- Location: Salvador, Bahia
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3. **Instituto Histórico e Geográfico de Alagoas**
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- Type: RESEARCH_CENTER
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- Wikidata: Q4086900
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- Match: Historical institute + geographic validation
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- Location: Maceió, Alagoas
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### Chile
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4. **Museo Nacional de Historia Natural de Chile**
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- Type: MUSEUM
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- Wikidata: Q2417662
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- Match: National museum + entity type validation
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- Location: Santiago
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5. **Archivo Nacional de Chile**
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- Type: ARCHIVE
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- Wikidata: Q2861466
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- Match: National archive + country validation
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- Location: Santiago
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### Mexico
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6. **Museo Nacional de Antropología**
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- Type: MUSEUM
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- Wikidata: Q191288
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- Match: Major national museum (exact match)
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- Location: Mexico City
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7. **Biblioteca Nacional de México**
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- Type: LIBRARY
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- Wikidata: Q640694
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- Match: National library (exact match)
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- Location: Mexico City
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---
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## Challenges & Limitations
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### 1. **Small Regional Museums**
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- **Problem**: Many small municipal museums lack Wikidata entries
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- **Example**: "Museu Municipal de Cidade Pequena" (Municipal Museum of Small Town)
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- **Solution**: Manual Wikidata entry creation or expansion of regional museum documentation
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### 2. **Generic Names**
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- **Problem**: "Centro Cultural" (Cultural Center) is too generic for disambiguation
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- **Example**: Multiple institutions named "Casa de Cultura" in different cities
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- **Solution**: Enhanced geographic validation + additional context (founding year, parent organization)
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### 3. **Recent Institutions**
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- **Problem**: Cultural centers established after 2015 may not be in Wikidata yet
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- **Example**: New digital heritage platforms, temporary exhibition spaces
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- **Solution**: Community contribution to Wikidata or wait for organic growth
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### 4. **Language Barrier**
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- **Problem**: Some Portuguese/Spanish names don't have English equivalents in Wikidata
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- **Example**: "Museu dos Povos Acreanos" (Museum of Acrean Peoples) - highly specific regional name
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- **Solution**: Automatic translation + alternative name generation worked in many cases
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### 5. **Education Providers**
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- **Problem**: Schools and training centers rarely documented as heritage institutions
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- **Success Rate**: Only 10.5% (4/38)
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- **Reason**: Wikidata focuses on primary functions (education) rather than secondary heritage roles
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- **Solution**: Re-classify as EDUCATION_PROVIDER + MUSEUM/LIBRARY if they have significant collections
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---
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## Recommendations
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### For Immediate Follow-up
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1. **✅ Manual Review of High-Value Missing Institutions**
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- Focus on national museums, major archives, and university libraries without Wikidata
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- Estimate: 20-30 institutions worth manual Wikidata entry creation
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2. **✅ Expand Alternative Names**
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- Add more regional language variants (indigenous language names, historical names)
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- Example: "Museo Nacional de Antropología" → "National Museum of Anthropology", "Museu Nacional de Antropologia"
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3. **✅ Re-run with Relaxed Thresholds**
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- Lower fuzzy match threshold from 70% to 65% for remaining 131 institutions
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- Add more entity type variants (e.g., MIXED → cultural centers, galleries, heritage sites)
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4. **✅ Cross-Reference with VIAF**
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- Some institutions may have VIAF IDs that link to Wikidata
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- Run VIAF enrichment pass before second Wikidata attempt
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### For Long-term Improvement
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5. **🌐 Community Wikidata Contribution Campaign**
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- Identify 50-100 notable Latin American institutions missing from Wikidata
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- Create Wikidata entries with structured data (founding year, location, collection type, etc.)
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- Coordinate with Latin American GLAM community (REDLAD, Ibermuseos)
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6. **📊 Comparative Analysis with Other Regions**
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- Run same enrichment on other geographic clusters (Southeast Asia, Eastern Europe, Middle East)
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- Document which factors predict enrichment success (baseline coverage, language, institution type)
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7. **🔗 Integrate with National Heritage Registries**
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- Brazil: Explore IBRAM (Brazilian Institute of Museums) registry
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- Mexico: INAH (National Institute of Anthropology and History) database
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- Chile: DIBAM (Directorate of Libraries, Archives and Museums) - now divided into separate agencies
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---
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## Files Updated
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1. **Input File**: `data/instances/latin_american_institutions_AUTHORITATIVE.yaml`
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- Metadata updated with enrichment statistics
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- 117 institutions gained Wikidata identifiers
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- Provenance tracking updated
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2. **Backup Created**: `data/instances/latin_american_institutions_AUTHORITATIVE.backup_20251106_124619.yaml`
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- Pre-enrichment state preserved
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3. **Script**: `scripts/enrich_latam_alternative_names.py`
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- 580 lines, based on Tunisia enrichment script
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- Automatic alternative name generation
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- Country-specific Wikidata queries
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4. **Documentation**:
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- This file: `docs/latam_enrichment_summary.md`
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- Reference: `docs/tunisia_enrichment_summary.md` (original methodology)
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---
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## Conclusion
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The Latin America Wikidata enrichment successfully applied the Tunisia methodology to a much larger and more diverse dataset, achieving:
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- **38.5 percentage point improvement** (18.4% → 56.9%)
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- **117 new Wikidata identifiers** added
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- **Chile reached 84.4% coverage** (best in Latin America)
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- **Brazil improved 35x** (from 1% to 36.1%)
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The strategy proved **highly effective across different languages, countries, and institution types**, validating the approach for global GLAM data enrichment.
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**Next Steps**:
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1. Update `PROGRESS.md` with these results
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2. Apply same methodology to remaining geographic clusters (Africa, Asia, Middle East)
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3. Contribute missing institutions to Wikidata for long-term ecosystem improvement
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---
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**Author**: GLAM Data Extraction Project
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**Date**: November 11, 2025
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**Version**: 1.0
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**Schema**: LinkML v0.2.1
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