kempersc 3c80de87e0 add isil entries

2025-11-19 23:25:22 +01:00

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Wikidata Fuzzy Match Validation Checklist

Overview

This checklist helps reviewers manually validate fuzzy Wikidata matches (85-99% confidence) to ensure data quality before RDF publication.

Dataset: Danish GLAM institutions
Fuzzy Matches: 185 institutions with match scores 85-99%
Review File: data/review/denmark_wikidata_fuzzy_matches.csv

Quick Start

1. Generate Review Report

python scripts/generate_wikidata_review_report.py

Output: data/review/denmark_wikidata_fuzzy_matches.csv

2. Open CSV in Spreadsheet Software

Excel: Open directly, enable UTF-8 encoding
Google Sheets: File → Import → Upload CSV
LibreOffice Calc: Open, select UTF-8 encoding

3. Review Priority Order

Start with Priority 1 (most uncertain) matches and work downward:

Priority	Score Range	Count	Description
1	85-87%	Varies	Very uncertain - review first
2	87-90%	Varies	Uncertain - needs verification
3	90-93%	Varies	Moderate confidence
4	93-96%	Varies	Fairly confident
5	96-99%	Varies	Mostly confident

Validation Process

For Each Row:

Step 1: Compare Names

Check: Does institution_name match wikidata_label?

Examples:

✅ CORRECT Match:

Institution: "Nationalmuseet"
Wikidata: "National Museum of Denmark"
Verdict: Different languages, same entity → CORRECT

✅ CORRECT Match:

Institution: "Rigsarkivet"
Wikidata: "Danish National Archives"
Verdict: Official name + English translation → CORRECT

❌ INCORRECT Match:

Institution: "Bibliotek Svendborg"
Wikidata: "Svendborg Museum"
Verdict: Library vs Museum → INCORRECT

⚠️ UNCERTAIN Match:

Institution: "Herning Bibliotekerne"
Wikidata: "Herning Libraries"
Verdict: Plural form, ambiguous → UNCERTAIN (needs further research)

Step 2: Verify on Wikidata

Click the wikidata_url link to open the Wikidata entity page.

Check:

Instance of (P31): Does the Wikidata type match institution_type?
- LIBRARY → Check for Q7075 (library) or subclass
- ARCHIVE → Check for Q166118 (archive) or subclass
- MUSEUM → Check for Q33506 (museum) or subclass
Country (P17): Should be Q35 (Denmark)
ISIL code (P791): If present, compare with isil_code column
- ✅ Match = high confidence CORRECT
- ❌ Mismatch = likely INCORRECT
Located in (P131): Should match or be near city column
Coordinates (P625): Check on Wikidata map if location makes sense
Official website (P856): Visit to confirm institution identity

Step 3: Research If Uncertain

When to research further:

Names are similar but not identical
Institution type unclear on Wikidata
No ISIL code to cross-reference
Multiple institutions with similar names in same city

Research Sources:

Danish ISIL Registry: https://isil.dk
- Official authoritative source
- Search by institution name or ISIL code
Institution Website: Google [institution_name] [city] Denmark
- Check "About" page
- Verify address, type, mission
Danish Library Directory: https://bibliotek.dk
- For libraries only
- Search by name or location
Danish National Archives: https://www.sa.dk
- For archives only
- Check member directory
Danish Agency for Culture and Palaces: https://slks.dk
- Government cultural institutions
- Official registries

Step 4: Record Validation Decision

Fill in the CSV columns:

validation_status: Choose ONE:

CORRECT - Confirmed match, keep Wikidata link
INCORRECT - Wrong match, remove Wikidata link
UNCERTAIN - Needs expert review, flag for follow-up

validation_notes: Add explanation (optional but recommended)

Examples:

validation_status,validation_notes
CORRECT,"ISIL codes match (DK-870970), same location"
INCORRECT,"Wrong institution - Wikidata is museum, we have library"
UNCERTAIN,"Similar names but no ISIL to confirm, needs local knowledge"
CORRECT,"Verified on official website, same address"
INCORRECT,"Wikidata entity is defunct branch, ours is current main library"

Common Validation Scenarios

Scenario 1: Danish vs English Names

Problem: Institution name in Danish, Wikidata label in English

Example:

Institution: "Kgl. Bibliotek"
Wikidata: "Royal Danish Library"

Solution:

Check Wikidata for Danish label (alias)
If match found → CORRECT
Add note: "Danish/English name variant"

Scenario 2: Historical Name Changes

Problem: Institution renamed, Wikidata has old name

Example:

Institution: "Københavns Bibliotek" (current)
Wikidata: "Copenhagen Public Library" (outdated label)

Solution:

Check Wikidata history/aliases
If same entity, different era → CORRECT
Add note: "Historical name, now [current name]"
Optionally: Edit Wikidata to add current name as alias

Scenario 3: Merged Institutions

Problem: Two institutions merged, Wikidata shows pre-merger entity

Example:

Institution: "Statsbiblioteket" (merged into KB)
Wikidata: "State and University Library" (defunct)

Solution:

Check Wikidata for "replaced by" (P1366) or "end time" (P582)
If defunct and our data is current → INCORRECT
Add note: "Wikidata entity is defunct, merged into [new name] in [year]"

Scenario 4: Branch vs Main Library

Problem: Wikidata refers to branch, our data is main library (or vice versa)

Example:

Institution: "Aarhus Bibliotek" (main)
Wikidata: "Aarhus Public Libraries" (system)

Solution:

Check Wikidata for "part of" (P361) relationship
If Wikidata is parent system → CORRECT (acceptable level)
If Wikidata is specific branch → UNCERTAIN
Add note: "Wikidata is [parent/branch], check hierarchy"

Scenario 5: Missing ISIL Codes

Problem: Institution has no ISIL code, can't cross-reference

Solution:

Rely on city + name + institution type match
If all three match → CORRECT
If any mismatch → UNCERTAIN
Add note: "No ISIL to verify, based on [city/name/type] match"

Batch Validation Tips

For Large Datasets (100+ matches):

Sort by Priority: Review Priority 1 first (most uncertain)
Filter by Institution Type: Review all libraries, then all archives
- Easier to maintain context
- Faster lookup in type-specific registries
Group by City: Review all institutions in same city together
- Easier to distinguish similar names
- Local knowledge helps
Use Find & Replace for common notes:
- "ISIL match confirmed" (for CORRECT + ISIL match)
- "Name variant, same entity" (for CORRECT + translation)
- "Type mismatch" (for INCORRECT + wrong type)
Mark Quick Wins First:
- ISIL codes match → Instant CORRECT
- Type mismatch (Library vs Museum) → Instant INCORRECT
- Then tackle ambiguous cases

Quality Assurance Checks

Before Running Update Script:

✅ Completeness: All Priority 1-2 rows have validation_status

✅ Consistency:

CORRECT matches should have high confidence reasons (ISIL, website, type match)
INCORRECT matches should have clear explanation (type mismatch, wrong entity)
UNCERTAIN should be rare (<5% of total)

✅ Documentation: At least 50% of rows have validation_notes

✅ Spelling: validation_status values are exact:

✅ CORRECT (all caps)
❌ Correct, correct, OK, YES

✅ No Blanks: Ensure no accidentally deleted data in other columns

Apply Validated Results

After Completing Manual Review:

# Apply validation results to dataset
python scripts/apply_wikidata_validation.py

Output: data/instances/denmark_complete_validated.json

Statistics Expected:

CORRECT: ~85-90% of reviewed (keep Wikidata link)
INCORRECT: ~5-10% of reviewed (remove Wikidata link)
UNCERTAIN: ~5% of reviewed (flag for expert review)

Re-export RDF

After Validation Applied:

# Re-export RDF with corrected Wikidata links
python scripts/export_denmark_rdf.py \
  --input data/instances/denmark_complete_validated.json \
  --output data/rdf/denmark_validated

Result: New RDF files with only verified Wikidata links

Validation Metrics

Track Your Progress:

Metric	Target	Actual
Priority 1 reviewed	100%	___
Priority 2 reviewed	100%	___
Priority 3 reviewed	80%	___
Priority 4-5 reviewed	50%	___
Incorrect matches found	5-10%	___
Uncertain matches	<5%	___
Time spent (hours)	-	___

Common Mistakes to Avoid

❌ DON'T: Mark as CORRECT just because names are similar
✅ DO: Verify at least 2 data points (city + type, or ISIL + name)

❌ DON'T: Mark as INCORRECT without checking Wikidata page
✅ DO: Always visit wikidata_url to verify entity details

❌ DON'T: Leave validation_notes blank for INCORRECT
✅ DO: Explain why match is wrong ("type mismatch", "different location", etc.)

❌ DON'T: Use UNCERTAIN as default when unsure
✅ DO: Research further using Danish registries before marking UNCERTAIN

❌ DON'T: Assume English Wikipedia = Wikidata label
✅ DO: Check Wikidata directly for Danish labels (aliases)

Escalation Process

When to Ask for Help:

>20% marked UNCERTAIN - May indicate systematic issue with matching algorithm
Local Knowledge Required - Danish language expertise needed for name variants
Conflicting Sources - ISIL registry vs Wikidata vs institution website disagree
Legal/Historical Complexity - Mergers, acquisitions, name changes unclear

Contact: [Project maintainer email/Slack/GitHub issue]

Example Validation Session

Sample Review (5 institutions):

Row 1:

Institution: "Aalborg Bibliotek"
Wikidata: "Aalborg Public Library"
Match Score: 86%
ISIL: DK-820010 (match found on Wikidata)
Decision: CORRECT
Notes: "ISIL match confirmed, Danish/English variant"

Row 2:

Institution: "Arkiv Svendborg"
Wikidata: "Svendborg Museum"
Match Score: 87%
Type: ARCHIVE vs MUSEUM (mismatch)
Decision: INCORRECT
Notes: "Type mismatch - archive vs museum"

Row 3:

Institution: "Herning Bibliotekerne"
Wikidata: "Herning Centralbibliotek"
Match Score: 88%
City: Herning (match)
Decision: UNCERTAIN
Notes: "Central library vs library system, check hierarchy"

Row 4:

Institution: "Rigsarkivet"
Wikidata: "Danish National Archives"
Match Score: 92%
ISIL: DK-011 (match), Official website verified
Decision: CORRECT
Notes: "Official name + English translation, website confirmed"

Row 5:

Institution: "Bibliotek Brønderslev"
Wikidata: "Brønderslev Library"
Match Score: 95%
City: Brønderslev (match), Type: LIBRARY (match)
Decision: CORRECT
Notes: "City and type match, high confidence"

Post-Validation Documentation

Update Project Documentation:

PROGRESS.md: Add validation statistics
SESSION_SUMMARY: Document findings and corrections
data/rdf/README.md: Note dataset version with validation applied
Commit Message: "feat: Apply manual Wikidata validation (185 fuzzy matches reviewed)"

References

Wikidata SPARQL Query Service: https://query.wikidata.org
Wikidata Property Browser: https://www.wikidata.org/wiki/Special:ListProperties
Danish ISIL Registry: https://isil.dk
RapidFuzz Library (matching algorithm): https://github.com/maxbachmann/RapidFuzz
Fuzzy String Matching: https://en.wikipedia.org/wiki/Approximate_string_matching

Version: 1.0
Last Updated: 2025-11-19
Maintained By: GLAM Data Extraction Project

12 KiB Raw Blame History

Wikidata Fuzzy Match Validation Checklist

Overview

Quick Start

1. Generate Review Report

2. Open CSV in Spreadsheet Software

3. Review Priority Order

Validation Process

For Each Row:

Step 1: Compare Names

Step 2: Verify on Wikidata

Step 3: Research If Uncertain

Step 4: Record Validation Decision

Common Validation Scenarios

Scenario 1: Danish vs English Names

Scenario 2: Historical Name Changes

Scenario 3: Merged Institutions

Scenario 4: Branch vs Main Library

Scenario 5: Missing ISIL Codes

Batch Validation Tips

For Large Datasets (100+ matches):

Quality Assurance Checks

Before Running Update Script:

Apply Validated Results

After Completing Manual Review:

Statistics Expected:

Re-export RDF

After Validation Applied:

Validation Metrics

Track Your Progress:

Common Mistakes to Avoid

Escalation Process

When to Ask for Help:

Example Validation Session

Sample Review (5 institutions):

Post-Validation Documentation

Update Project Documentation:

References

12 KiB

Raw Blame History