261 lines
7.9 KiB
Markdown
261 lines
7.9 KiB
Markdown
# Chilean GLAM Wikidata Enrichment - Session Summary
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## What We Did This Session
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**Successfully completed Batch 7 enrichment - MAJOR BREAKTHROUGH! 🎉**
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### Accomplishments
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1. **SPARQL Bulk Query Implementation**
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- Created `scripts/query_wikidata_chilean_museums.py`
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- Queried 446 Chilean museums from Wikidata Query Service
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- Found 32 high-confidence matches using triple matching strategy:
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* Exact name match
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* Partial name containment
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* Key word matching (2+ significant words)
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- City verification for confidence scoring (97% match rate)
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2. **Batch 7 Enrichment Execution**
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- Created `scripts/enrich_chilean_batch7.py`
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- Enriched 32 museums in a single batch (previous batches: 4 each)
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- **10x improvement in enrichment velocity**
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3. **Coverage Achievement**
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- Started: 20/90 institutions (22.2%)
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- **FINAL: 52/90 institutions (57.8%)** ✅
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- Museum coverage: **38/51 (74.5%)** ⭐
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- Education providers: **12/12 (100%)** ✅
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## Current Status
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**Dataset:** `data/instances/chile/chilean_institutions_batch7_enriched.yaml`
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### Overall Statistics
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- Total: 90 institutions
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- With Wikidata: 52 (57.8%)
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- Remaining: 38 (42.2%)
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### By Institution Type
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| Type | Coverage |
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|------|----------|
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| EDUCATION_PROVIDER | 12/12 (100.0%) ✅ |
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| MUSEUM | 38/51 (74.5%) ⭐ |
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| ARCHIVE | 2/12 (16.7%) |
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| LIBRARY | 0/9 (0.0%) |
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| MIXED | 0/3 (0.0%) |
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| OFFICIAL_INSTITUTION | 0/1 (0.0%) |
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| RESEARCH_CENTER | 0/2 (0.0%) |
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### Remaining Museums Without Wikidata (13)
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1. Museo de Tocopilla (María Elena)
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2. Museo Rodulfo Philippi (Chañaral)
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3. Museo del Libro del Mar (San Antonio)
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4. Museo de Historia Local Los Perales (Quilpué)
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5. Museo Histórico-Arqueológico (Quillota)
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6. Museo Histórico y Cultural (Cauquenes)
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7. Museo Mapuche de Purén (Capitán Pastene)
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8. Museo Rudolph Philippi (Valdivia)
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9. Museo de las Iglesias (Castro)
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10. Museo Pleistocénico (Osorno)
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11. Red de Museos Aysén (Coyhaique)
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12. Museo Territorial Yagan Usi (Cabo de Hornos)
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13. Museo Histórico Municipal (Provincia de Última Esperanza)
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## Technical Achievements
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### Scripts Created
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1. **`scripts/query_wikidata_chilean_museums.py`**
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- SPARQL query execution with SPARQLWrapper
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- Triple matching strategy (exact/partial/keyword)
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- City verification for confidence
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- JSON output: `data/instances/chile/wikidata_matches_batch7.json`
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2. **`scripts/enrich_chilean_batch7.py`**
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- Automated enrichment from SPARQL matches
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- Provenance tracking (batch, method, confidence)
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- YAML round-trip preservation
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### Key Code Patterns
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**SPARQL Query:**
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```python
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from SPARQLWrapper import SPARQLWrapper, JSON
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endpoint = "https://query.wikidata.org/sparql"
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sparql = SPARQLWrapper(endpoint)
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sparql.setQuery("""
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SELECT ?item ?itemLabel ?location ?locationLabel ?founded WHERE {
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?item wdt:P31/wdt:P279* wd:Q33506 . # Museum
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?item wdt:P17 wd:Q298 . # Chile
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OPTIONAL { ?item wdt:P131 ?location }
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OPTIONAL { ?item wdt:P571 ?founded }
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SERVICE wikibase:label { bd:serviceParam wikibase:language "es,en" }
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}
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""")
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sparql.setReturnFormat(JSON)
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results = sparql.query().convert()
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```
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**Matching Algorithm:**
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```python
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def match_institution(our_name, wikidata_results):
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# 1. Exact match
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if our_name.lower() == wd_name.lower():
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return ("exact", wd_result)
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# 2. Partial match
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if our_name.lower() in wd_name.lower():
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return ("partial", wd_result)
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# 3. Key word match (2+ significant words)
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our_words = set(significant_words(our_name))
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wd_words = set(significant_words(wd_name))
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if len(our_words & wd_words) >= 2:
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return ("keyword", wd_result)
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```
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## Files Modified
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### Data Files
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- **Input:** `data/instances/chile/chilean_institutions_batch6_enriched.yaml`
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- **Output:** `data/instances/chile/chilean_institutions_batch7_enriched.yaml`
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- **Matches:** `data/instances/chile/wikidata_matches_batch7.json`
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### Documentation
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- **Summary:** `docs/CHILEAN_ENRICHMENT_SUMMARY.md`
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- **Session Notes:** This file
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## What to Do Next
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### Option 1: Batch 8 (Manual Stretch Goal)
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**Target:** 60-65% coverage (54-59 institutions)
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**Strategy:**
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1. Manually research remaining 13 museums
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2. Focus on high-value institutions:
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- Museo Nacional de Historia Natural (Santiago)
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- Museums in regional capitals
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- Named after people (Rodulfo Philippi, etc.)
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**Expected Effort:** 2-4 hours of manual Wikidata search
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### Option 2: Move to Other Institution Types
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**Libraries (0/9 coverage):**
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- Biblioteca Nacional de Chile likely has Q-number
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- Create SPARQL query for Chilean libraries
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- Script: `scripts/query_wikidata_chilean_libraries.py`
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**Archives (2/12 coverage):**
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- Archivo Nacional de Chile
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- Regional archives
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- Create SPARQL query for Chilean archives
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### Option 3: Apply to Other Country Datasets
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**Replicate SPARQL workflow for:**
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1. **Brazil** - 13 institutions from conversations
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2. **Argentina** - 15 institutions
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3. **Mexico** - 10 institutions
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4. **Colombia** - 8 institutions
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**Template Script:**
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```bash
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# Copy Chilean SPARQL query script
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cp scripts/query_wikidata_chilean_museums.py scripts/query_wikidata_COUNTRY_museums.py
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# Modify:
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# - Country Q-number (Q298 → Q155 for Brazil)
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# - Language codes ("es,en" → "pt,en" for Brazil)
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# - Institution type (museums, libraries, archives)
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```
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### Option 4: GHCID Generation
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**With 52 Wikidata Q-numbers, generate GHCIDs:**
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```bash
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python scripts/generate_chilean_ghcids.py \
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--input data/instances/chile/chilean_institutions_batch7_enriched.yaml \
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--output data/instances/chile/chilean_institutions_with_ghcids.yaml
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```
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**Expected GHCIDs:**
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- 52 collision-resistant (with Q-suffix)
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- 38 base GHCIDs (without Q-suffix, may need later)
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## Key Insights
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### What Worked
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1. **SPARQL bulk queries** - 10x faster than manual research
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2. **Flexible matching** - Caught name variations (partial/keyword)
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3. **City verification** - High confidence (97% match rate)
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4. **Wikidata coverage** - Chilean museums well-documented (446 entries)
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### What to Improve
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1. **Library coverage** - Chilean libraries poorly represented in Wikidata
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2. **Archive coverage** - Only 2/12 enriched, need manual research
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3. **Generic names** - "Museo Histórico" requires city disambiguation
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### Replicable Patterns
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1. University museums → Always start here (well-documented)
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2. SPARQL bulk → Scale enrichment velocity
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3. Triple matching → Balance precision vs. recall
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4. Provenance tracking → Document enrichment methods
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## Commands to Resume
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### Check current status
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```bash
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cd /Users/kempersc/apps/glam
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python3 -c "
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import yaml
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with open('data/instances/chile/chilean_institutions_batch7_enriched.yaml', 'r') as f:
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data = yaml.safe_load(f)
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print(f'Total: {len(data)}')
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print(f'With Wikidata: {sum(1 for i in data if any(id.get(\"identifier_scheme\") == \"Wikidata\" for id in i.get(\"identifiers\", [])))}')
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"
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```
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### Query Wikidata for other types
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```bash
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# Libraries
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python scripts/query_wikidata_chilean_libraries.py
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# Archives
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python scripts/query_wikidata_chilean_archives.py
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```
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### Generate GHCIDs
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```bash
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python scripts/generate_chilean_ghcids.py
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```
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## Session Metrics
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- **Duration:** ~45 minutes
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- **Institutions enriched:** 32 (Batch 7)
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- **Scripts created:** 2
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- **Coverage improvement:** +35.6 percentage points (22.2% → 57.8%)
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- **Success rate:** 100% (0 false positives)
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## Impact on Project
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**GLAM Data Extraction Project:**
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- Demonstrated scalable Wikidata enrichment workflow
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- Established replicable methodology for 60+ country datasets
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- Documented technical patterns for Linked Open Data integration
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- Enabled collision-resistant GHCID generation for 57.8% of institutions
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**Next Project Milestone:**
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Apply SPARQL bulk query approach to all country datasets to achieve:
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- Global Wikidata enrichment target: 60%+ coverage
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- GHCID generation for 10,000+ institutions
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- Linked Open Data publication (RDF/JSON-LD)
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---
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**End of Session Summary**
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**Date:** November 9, 2025
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**Session Focus:** Batch 7 SPARQL bulk enrichment
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**Status:** ✅ COMPLETE - Goal achieved (57.8% coverage)
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