# Automated Spot Check Results - Danish Wikidata Fuzzy Matches

**Date**: 2025-11-19  
**Method**: Fast pattern-based detection (no Wikidata API queries)  
**Total Matches**: 185  
**Flagged Issues**: 129 (69.7%)  
**No Issues**: 56 (30.3%)

---

## 🎯 Executive Summary

Automated checks identified **71 obvious city mismatches** that are almost certainly INCORRECT matches. These can be quickly marked INCORRECT without manual research, reducing review time significantly.

### Key Findings

| Category | Count | Confidence | Action |
|----------|-------|------------|--------|
| 🚨 **City Mismatches** | **71** | Very High | Mark INCORRECT immediately |
| 🔍 Kombi Library Mismatches | 1 | Moderate | Needs judgment |
| 🔍 Low Name Similarity (<60%) | 11 | Moderate | Needs judgment |
| 🔍 Gymnasium Libraries | 7 | Moderate | Usually INCORRECT |
| 🔍 Other Name Pattern Issues | 39 | Low | Needs case-by-case review |
| ✅ No Issues Detected | 56 | N/A | Spot check Priority 1-2 only |

---

## 🚨 Priority 1: City Mismatches (71 matches) - MARK AS INCORRECT

**Confidence**: 95%+ these are wrong matches  
**Time Required**: ~71 minutes (1 min each)  
**Action**: Open CSV, filter for "🚨 City mismatch", mark validation_status = INCORRECT

### Examples:

1. **Gladsaxe Bibliotekerne** (Søborg) matched to **Gentofte Bibliotekerne** (Gentofte)
   - Different cities → INCORRECT

2. **Fur Lokalhistoriske Arkiv** (Skive) matched to **Randers Lokalhistoriske Arkiv** (Randers)
   - Different cities → INCORRECT

3. **Rysensteen Gymnasium** (København V) matched to **Greve Gymnasium** (Greve)
   - Different cities → INCORRECT

4. **Multiple "X Lokalhistoriske Arkiv"** matched to **Randers Lokalhistoriske Arkiv**
   - Algorithm confused similar names in different cities → INCORRECT

**Pattern**: The fuzzy matching algorithm matched institutions with similar names but in completely different cities. These are clearly distinct institutions.

**Validation Notes Template**:
```
City mismatch detected by automated spot check: our institution in [City A] but Wikidata entity in [City B]. Different institutions.
```

---

## 🔍 Priority 2: Low Name Similarity (11 matches) - NEEDS JUDGMENT

**Confidence**: 60-70% likely INCORRECT  
**Time Required**: ~22 minutes (2 min each)  
**Action**: Review each, check Wikidata page for verification

### Examples:

1. **Campus Vejle, Biblioteket** (58% similarity) vs **Vejle Bibliotek**
   - Possibly campus branch vs main library?
   - Check Wikidata P361 (part of) property

2. **Lunds stadsbibliotek** vs **Billund Bibliotek**
   - Very different names, likely wrong match
   - "Lunds" suggests Sweden, not Denmark?

**Validation Steps**:
1. Visit wikidata_url
2. Check P131 (located in) - does city match?
3. Check P361 (part of) - is one a branch of the other?
4. Mark CORRECT if branch/main relationship, INCORRECT if completely different

---

## 🏫 Priority 3: Gymnasium Libraries (7 matches) - USUALLY INCORRECT

**Confidence**: 70-80% likely INCORRECT  
**Time Required**: ~14 minutes (2 min each)  
**Action**: Verify if school library vs public library

### Pattern:

**Our Name**: "[School Name] Gymnasium, Biblioteket"  
**Wikidata**: "[City Name] Bibliotek" (public library)

**Issue**: School libraries matched to public libraries in same city.

**Examples**:
- Fredericia Gymnasium, Biblioteket → Fredericia Bibliotek
- Viborg Handelsskole, Biblioteket → Viborg Bibliotek

**Check**:
1. Visit Wikidata page
2. Look for P31 (instance of) - should show "public library" or "school library"
3. If Wikidata is public library and ours is gymnasium → INCORRECT
4. If Wikidata is also school library → CORRECT

---

## 🔍 Priority 4: Other Flagged Issues (40 matches) - CASE BY CASE

**Confidence**: Varies  
**Time Required**: ~80 minutes (2 min each)  
**Action**: Review based on specific issue type

**Issue Types**:
- Branch suffix ", Biblioteket" in our name
- First word differs (possible city mismatch)
- Low score (<87%) without ISIL confirmation
- Kombi library location mismatches

**Approach**: Follow validation checklist for each match.

---

## ✅ Priority 5: No Issues Detected (56 matches) - LOWER PRIORITY

**Confidence**: 80-90% likely CORRECT  
**Time Required**: ~28 minutes (spot check only)  
**Action**: Spot check Priority 1-2 matches, skip Priority 3-5

These matches passed all automated checks:
- Cities match or no conflict detected
- Names reasonably similar (>60%)
- No obvious type mismatches
- No problematic patterns

**Recommendation**: 
- Review Priority 1-2 "no issues" matches (30-40 matches)
- Skip Priority 3-5 "no issues" matches (high confidence)
- Estimated time: ~15-20 minutes

---

## ⏱️ Time Estimates

| Task | Matches | Time/Match | Total Time |
|------|---------|------------|------------|
| **City Mismatches** (mark INCORRECT) | 71 | 1 min | 71 min |
| **Low Similarity** (review) | 11 | 2 min | 22 min |
| **Gymnasium Libraries** (review) | 7 | 2 min | 14 min |
| **Other Flagged** (review) | 40 | 2 min | 80 min |
| **No Issues P1-2** (spot check) | 30 | 0.5 min | 15 min |
| **────────────────** | **───** | **─────** | **──────** |
| **TOTAL** | **159** | **avg 1.3 min** | **~3.4 hours** |

**Original Estimate**: 5-8 hours for all 185 matches  
**Revised with Automation**: ~3.4 hours (57% time savings!)

---

## 📋 Step-by-Step Workflow

### Step 1: Open Flagged CSV

```bash
open data/review/denmark_wikidata_fuzzy_matches_flagged.csv
```

### Step 2: Mark City Mismatches (71 matches, 1 hour)

1. Sort by `spot_check_issues` column
2. Filter for rows containing "🚨 City mismatch"
3. For each row:
   - Fill `validation_status` = `INCORRECT`
   - Fill `validation_notes` = `City mismatch: [our city] vs [Wikidata city], different institutions`
4. Save CSV

### Step 3: Review Low Similarity (11 matches, 22 min)

1. Filter for "Low name similarity"
2. For each row:
   - Click `wikidata_url`
   - Check P131 (location), P361 (part of)
   - Decide: CORRECT (branch/main) or INCORRECT (different)
   - Fill `validation_status` and `validation_notes`

### Step 4: Review Gymnasium Libraries (7 matches, 14 min)

1. Filter for "Gymnasium"
2. For each row:
   - Click `wikidata_url`
   - Check P31 (instance of) - public vs school library?
   - If mismatch → INCORRECT
   - Fill `validation_status` and `validation_notes`

### Step 5: Review Other Flagged (40 matches, 80 min)

1. Filter for remaining `REVIEW_URGENT` rows
2. Follow validation checklist for each
3. Fill `validation_status` and `validation_notes`

### Step 6: Spot Check "No Issues" (30 matches, 15 min)

1. Filter for `auto_flag = OK` AND `priority IN (1, 2)`
2. Quick review (30 sec each):
   - Names look similar? → CORRECT
   - Any obvious issues? → INCORRECT
3. Fill `validation_status`

### Step 7: Apply Validation

```bash
python scripts/apply_wikidata_validation.py
```

### Step 8: Check Results

```bash
python scripts/check_validation_progress.py
```

---

## 📊 Expected Validation Results

Based on automated spot check findings:

| Status | Expected Count | % of Total | Notes |
|--------|----------------|------------|-------|
| **CORRECT** | 100-110 | 54-59% | No-issues matches + verified |
| **INCORRECT** | 70-80 | 38-43% | City mismatches + other errors |
| **UNCERTAIN** | 5-10 | 3-5% | Ambiguous cases |

**Quality Target**: ≥50% CORRECT, ≤45% INCORRECT (acceptable given fuzzy matching)

**Note**: Higher INCORRECT rate than original estimate (5-10%) because automated checks caught many city mismatch errors that would have required manual research.

---

## 🎓 Validation Decision Guide

### Mark as INCORRECT if:
- ✅ City names differ (e.g., Skive vs Randers)
- ✅ Institution type differs (library vs museum)
- ✅ Gymnasium library matched to public library
- ✅ Name similarity <50% with no other confirmation

### Mark as CORRECT if:
- ✅ ISIL codes match (authoritative)
- ✅ Branch relationship confirmed on Wikidata (P361)
- ✅ Same institution, different language (Danish/English)
- ✅ Name similarity >70% AND city matches

### Mark as UNCERTAIN if:
- ⚠️ Cannot determine branch vs main relationship
- ⚠️ Historical name change unclear
- ⚠️ No clear evidence either way

---

## 📁 Files Generated

### Input
- `data/review/denmark_wikidata_fuzzy_matches.csv` (original, 42 KB)

### Output
- `data/review/denmark_wikidata_fuzzy_matches_flagged.csv` (with spot check results, 57 KB)

### Scripts
- `scripts/spot_check_fuzzy_matches_fast.py` (pattern-based detection)
- `scripts/apply_wikidata_validation.py` (apply results after manual review)
- `scripts/check_validation_progress.py` (progress tracking)

---

## 🚀 Quick Start Command

```bash
# Open flagged CSV
open data/review/denmark_wikidata_fuzzy_matches_flagged.csv

# Sort by spot_check_issues column (🚨 city mismatches first)
# Mark all city mismatches as INCORRECT (validation_status = INCORRECT)
# Review remaining flagged rows
# Spot check "OK" rows in Priority 1-2
# Save CSV

# Apply validation
python scripts/apply_wikidata_validation.py

# Check progress
python scripts/check_validation_progress.py
```

---

## 🎯 Success Criteria

After manual review:

- [ ] All 71 city mismatches marked INCORRECT
- [ ] All 11 low similarity cases reviewed
- [ ] All 7 gymnasium libraries reviewed
- [ ] Priority 1-2 "OK" rows spot-checked
- [ ] At least 150/185 (81%) rows have validation_status
- [ ] At least 100/185 (54%) rows have validation_notes
- [ ] Apply script runs successfully
- [ ] Final dataset has <100 INCORRECT removals

---

## 📝 Sample Validation Notes

### For City Mismatches (INCORRECT)
```
Automated spot check detected city mismatch: our institution in Skive vs Wikidata entity in Randers. Different local historical archives.
```

### For Low Similarity (needs judgment)
```
Low name similarity (58%). Checked Wikidata - "Campus Vejle" is campus library branch, Wikidata entry is main public library. Different institutions. INCORRECT.
```

### For Gymnasium (INCORRECT)
```
School library (gymnasium) incorrectly matched to public library. Wikidata P31 shows "public library" but ours is "school library". INCORRECT.
```

### For Branch Relationships (CORRECT)
```
Branch library matched to main library. Checked Wikidata P361 - confirms branch relationship. Same institution system. CORRECT.
```

---

## 🔧 Troubleshooting

**Q: CSV won't sort by spot_check_issues?**  
A: Try filtering instead - Excel/Sheets: Data → Filter → Select "🚨 City mismatch"

**Q: Too many matches to review in one session?**  
A: Focus on city mismatches first (71 matches), complete in 1 session. Rest can wait.

**Q: Unsure about a match?**  
A: Mark as UNCERTAIN, add detailed notes. We can research further later.

**Q: How do I know if done?**  
A: Run `python scripts/check_validation_progress.py` - shows completion %

---

## 📈 Progress Tracking

Use this checklist:

```
[x] Automated spot checks run (129 flagged)
[ ] City mismatches reviewed (0/71)
[ ] Low similarity reviewed (0/11)
[ ] Gymnasium libraries reviewed (0/7)
[ ] Other flagged reviewed (0/40)
[ ] No-issues spot checked (0/30)
[ ] Validation applied
[ ] RDF re-exported

Current Progress: 0/159 (0%)
```

---

**Last Updated**: 2025-11-19  
**Generated By**: `scripts/spot_check_fuzzy_matches_fast.py`  
**Review CSV**: `data/review/denmark_wikidata_fuzzy_matches_flagged.csv`