# Japan Wikidata Enrichment Strategy

**Date**: 2025-11-20  
**Status**: Ready for execution  
**Script**: `scripts/enrich_japan_wikidata_real.py`

---

## Executive Summary

After cleaning 3,426 synthetic Q-numbers from the Japan dataset, we now need to perform **real Wikidata enrichment**. This document outlines the enrichment strategy, realistic expectations, and execution plan.

### Key Facts

| Metric | Value |
|--------|-------|
| **Institutions needing enrichment** | 3,426 |
| **Libraries** | 3,348 (97.7%) |
| **Museums** | 76 (2.2%) |
| **Archives** | 2 (0.1%) |
| **Estimated runtime** | ~1 hour 3 minutes |
| **Expected match rate** | 10-20% (~340-685 matches) |

---

## Enrichment Script Created

**File**: `scripts/enrich_japan_wikidata_real.py` (474 lines)

### Features

✅ **Real Q-numbers only** - Verifies every Q-number via Wikidata API  
✅ **Fuzzy matching** - Uses `rapidfuzz` with ≥85% similarity threshold  
✅ **Location verification** - Checks city/prefecture matches  
✅ **Multiple match algorithms** - ratio, partial_ratio, token_sort_ratio  
✅ **Rate limiting** - Respects Wikidata 1 req/sec limit  
✅ **SPARQL queries** - Fetches Japanese heritage institutions by type  
✅ **Comprehensive reporting** - Generates enrichment statistics  
✅ **Dry-run mode** - Test without modifying dataset  

### Usage

```bash
# Test with 10 institutions (dry run)
python scripts/enrich_japan_wikidata_real.py --dry-run --limit 10

# Process museums only (faster testing)
python scripts/enrich_japan_wikidata_real.py --dry-run --limit 76

# Full enrichment (all 3,426 institutions, ~1 hour)
python scripts/enrich_japan_wikidata_real.py

# Full enrichment with progress (recommended)
python scripts/enrich_japan_wikidata_real.py 2>&1 | tee enrichment_log.txt
```

---

## Realistic Expectations

### Why Most Won't Match

**97.7% of institutions needing enrichment are small libraries**:

- Sapporo Shinkotoni Library (branch library)
- ASAHIKAWASHICHUO Library (city library)
- KUSHIROSHITENJI Library (district library)
- KITAMISHIRITSUTANNO Library (municipal library)

**These typically do NOT have Wikidata entries because**:

1. **Not notable enough** - Wikidata inclusion criteria require notability
2. **Limited documentation** - Small local institutions lack English-language sources
3. **No external identifiers** - Local ISIL codes don't appear in Wikidata
4. **Wikidata focus** - Focuses on major institutions (national libraries, major museums)

### Expected Match Rates by Type

| Institution Type | Count | Expected Match Rate | Expected Matches |
|------------------|-------|---------------------|------------------|
| **Museums** | 76 | 30-50% | 23-38 |
| **Archives** | 2 | 50-100% | 1-2 |
| **Major Libraries** | ~50 | 20-40% | 10-20 |
| **Small Libraries** | ~3,298 | 5-10% | 165-330 |
| **Total** | 3,426 | 10-20% | **340-685** |

### Examples Likely to Match

✅ **Major Museums**:
- Fukushima Prefectural Museum of Art → likely Q-number
- Fukuoka Prefectural Museum of Art → likely Q-number
- Tokyo Station Gallery → likely Q-number
- Japan Olympic Museum → likely Q-number

✅ **Major Libraries**:
- Sapporo Central Library (if queried) → likely Q-number
- Hokkaido Prefectural Library → likely Q-number

❌ **Small Libraries** (won't match):
- Sapporo Shinkotoni Library → no Q-number
- ASAHIKAWASHICHUO Library → no Q-number
- Branch/district libraries → no Q-numbers

---

## Enrichment Workflow

### Step 1: Query Wikidata SPARQL

For each institution type (LIBRARY, MUSEUM, ARCHIVE):

```sparql
SELECT DISTINCT ?item ?itemLabel ?itemDescription ?isil ?viaf ?coords
WHERE {
  ?item wdt:P31/wdt:P279* wd:Q7075 .  # Instance of library
  ?item wdt:P17 wd:Q17 .                # Country: Japan
  OPTIONAL { ?item wdt:P791 ?isil }   # ISIL code
  OPTIONAL { ?item wdt:P214 ?viaf }   # VIAF ID
  OPTIONAL { ?item wdt:P625 ?coords } # Coordinates
  SERVICE wikibase:label { bd:serviceParam wikibase:language "ja,en" }
}
LIMIT 1000
```

**Expected results**:
- Libraries: ~800-1,000 Wikidata entities
- Museums: ~500-800 Wikidata entities
- Archives: ~100-200 Wikidata entities

### Step 2: Fuzzy Name Matching

For each institution, match against Wikidata candidates:

```python
# Calculate fuzzy match score
ratio = fuzz.ratio(institution_name, wikidata_label)
partial_ratio = fuzz.partial_ratio(institution_name, wikidata_label)
token_sort_ratio = fuzz.token_sort_ratio(institution_name, wikidata_label)

match_score = max(ratio, partial_ratio, token_sort_ratio)

# Bonus for location match
if city_matches or coordinates_nearby:
    match_score += 10

# Accept if score >= 85
if match_score >= 85:
    return WikidataMatch(...)
```

### Step 3: Verify Q-Number Exists

**CRITICAL**: Before adding Q-number to dataset, verify it exists:

```python
# Safety check via Wikidata API
response = requests.get(
    'https://www.wikidata.org/w/api.php',
    params={'action': 'wbgetentities', 'ids': q_number, 'format': 'json'}
)

if 'missing' in response.json()['entities'][q_number]:
    print(f"⚠️  Q-number {q_number} does NOT exist! Skipping.")
    return None  # Never add fake Q-numbers!
```

### Step 4: Add to Identifiers

If match verified, add to `identifiers` array:

```yaml
identifiers:
  - identifier_scheme: Wikidata
    identifier_value: Q12345678  # REAL Q-number (verified)
    identifier_url: https://www.wikidata.org/wiki/Q12345678
  - identifier_scheme: VIAF  # If found in Wikidata
    identifier_value: "123456789"
    identifier_url: https://viaf.org/viaf/123456789
```

### Step 5: Update Provenance

Document enrichment in provenance metadata:

```yaml
provenance:
  enrichment_history:
    - enrichment_date: "2025-11-20T..."
      enrichment_method: "Wikidata SPARQL query + fuzzy name matching"
      match_score: 92.5
      location_match: true
      verified: true
      q_number: Q12345678
      wikidata_label: "Fukushima Prefectural Museum of Art"
```

### Step 6: Remove Enrichment Flag

```yaml
# BEFORE
needs_wikidata_enrichment: true

# AFTER (if match found)
# (flag removed)

# AFTER (if no match found)
needs_wikidata_enrichment: true  # Keep flag
```

---

## Execution Plan

### Phase 1: Test Run (Completed)

✅ Created enrichment script (474 lines)  
✅ Tested with 10 institutions (dry run)  
✅ Script works correctly (no matches expected for small libraries)  
✅ Ready for full execution

### Phase 2: Full Enrichment (Next Step)

**Command**:
```bash
cd /Users/kempersc/apps/glam
python scripts/enrich_japan_wikidata_real.py 2>&1 | tee enrichment_log.txt
```

**Expected duration**: ~1 hour 3 minutes (rate limiting: 1.1 sec/institution)

**Expected output**:
```
================================================================================
Wikidata Enrichment - Real Q-Numbers Only
================================================================================

Total institutions: 12,065
Need enrichment: 3,426

Querying Wikidata for Japanese heritage institutions...
  Querying LIBRARY...
    Found 1000 Wikidata entities
  Querying MUSEUM...
    Found 650 Wikidata entities
  Querying ARCHIVE...
    Found 120 Wikidata entities

Total Wikidata candidates: 1,770

Processing institutions...

[1/3426] Sapporo Shinkotoni Library
  ⚠️  No match: Sapporo Shinkotoni Library

[2/3426] Sapporo Sumikawa Library
  ⚠️  No match: Sapporo Sumikawa Library

...

[523/3426] Fukushima Prefectural Museum of Art
  ✅ Match: Fukushima Prefectural Museum of Art
     → 福島県立美術館 (Q11638009)
     Score: 94.2% | Location: true

...

[3426/3426] Final institution
  ⚠️  No match: ...

================================================================================
ENRICHMENT COMPLETE
================================================================================

📊 Results:
   Total processed: 3,426
   Matches found: 487
   High confidence: 234 (≥90%)
   Medium confidence: 253 (85-89%)
   No match: 2,939

✅ Enriched dataset: jp_institutions_wikidata_enriched.yaml
✅ Enrichment report: WIKIDATA_ENRICHMENT_REPORT.md
```

### Phase 3: Review and Integration

After enrichment completes:

1. **Review enrichment report** - Check match statistics
2. **Spot-check matches** - Verify high-confidence matches are correct
3. **Replace original dataset** - Install enriched version as `jp_institutions_resolved.yaml`
4. **Rebuild unified database** - Rerun `unify_all_datasets.py`
5. **Update session summary** - Document enrichment results

---

## What Happens to Institutions Without Matches?

### Keep Base GHCIDs

Institutions that don't match Wikidata should **KEEP their base GHCIDs** (without Q-numbers):

```yaml
# Example: Small library with no Wikidata entry
- name: Sapporo Shinkotoni Library
  ghcid: JP-HO-SAP-L-SSL  # Base GHCID (no Q-number)
  needs_wikidata_enrichment: true  # Flag remains (legitimate absence)
  provenance:
    notes: >-
      No Wikidata match found during enrichment (2025-11-20).
      Institution is a small municipal library that does not meet
      Wikidata notability criteria. Base GHCID is appropriate.
```

### This is CORRECT Behavior

**Per AGENTS.md policy**:

> If no Wikidata Q-number is available:
> 1. Use base GHCID without Q-suffix (e.g., NL-NH-AMS-M-HM)
> 2. Flag institution with `needs_wikidata_enrichment: true`
> 3. Run Wikidata enrichment workflow to obtain real Q-number
> 4. **If enrichment finds no match, this is legitimate** - not all institutions have Q-numbers

### Don't Force Matches

❌ **NEVER**:
- Lower fuzzy match threshold below 85%
- Generate synthetic Q-numbers
- Create fake Wikidata entries
- Force matches when uncertain

✅ **ALWAYS**:
- Accept that small institutions may not have Q-numbers
- Keep base GHCIDs for institutions without matches
- Document enrichment attempts in provenance
- Consider creating Wikidata entries for notable missing institutions

---

## Post-Enrichment Actions

### If Match Rate is Low (<15%)

**Possible reasons**:
1. Most institutions are small libraries (expected)
2. Wikidata has limited coverage of Japanese local libraries
3. Name transliteration differences (romaji vs. kanji)

**Actions**:
1. ✅ Accept results - this is expected
2. ✅ Document in report that most non-matches are legitimate
3. ⚠️  Consider creating Wikidata entries for notable missing institutions
4. ⚠️  Improve name matching for Japanese characters (romaji/kanji variants)

### If Match Rate is High (>25%)

**Possible reasons**:
1. Dataset includes many major institutions
2. Wikidata has better coverage than expected
3. Fuzzy matching is working well

**Actions**:
1. ✅ Celebrate good results!
2. ✅ Spot-check high-confidence matches
3. ✅ Document successful enrichment

---

## Files Generated

| File | Purpose |
|------|---------|
| `scripts/enrich_japan_wikidata_real.py` | Enrichment script (474 lines) |
| `data/instances/japan/jp_institutions_wikidata_enriched.yaml` | Output (after running) |
| `data/instances/japan/WIKIDATA_ENRICHMENT_REPORT.md` | Statistics and analysis |
| `enrichment_log.txt` | Execution log with all matches/non-matches |

---

## Next Steps Summary

### Immediate (Now)

```bash
# Run full enrichment (takes ~1 hour)
cd /Users/kempersc/apps/glam
python scripts/enrich_japan_wikidata_real.py 2>&1 | tee enrichment_log.txt
```

### After Enrichment

1. Review `WIKIDATA_ENRICHMENT_REPORT.md`
2. Spot-check matches in `enrichment_log.txt`
3. Replace original with enriched dataset
4. Rebuild unified database
5. Update session documentation

---

## Data Integrity Guarantee

✅ **All Q-numbers added will be REAL**:
- Verified to exist via Wikidata API
- Fuzzy matched with ≥85% similarity
- Location-verified where possible
- Properly documented in provenance

❌ **Zero synthetic Q-numbers** will be generated

✅ **Institutions without matches will keep base GHCIDs** (appropriate behavior)

---

**Script ready**: `scripts/enrich_japan_wikidata_real.py`  
**Estimated runtime**: ~1 hour 3 minutes  
**Expected matches**: 340-685 (10-20%)  
**Data integrity**: 100% guaranteed