微信扫一扫Wikipedia Research Skill
Extract verifiable research from Wikipedia with full citation provenance, entity relationships, timelines, and verification reports for AI consumption.
Why Use This Skill?
| Without Skill | With Skill | |---------------|------------| | Unstructured prose | Structured JSON with schema | | "Various sources" | 12+ citations with DOIs, PMIDs | | Claims float freely | Every claim mapped to citations | | No verification possible | DOI/PMID validation included | | Unknown reliability | Admiralty Code quality rating | | No relationships | Entity + relationship extraction | | No timeline | Chronological event mapping | | Dead links undetected | Archive fallback included |
Complete Research Workflow
Phase 1: Extract
from scripts.citation_extractor import CitationExtractor
extractor = CitationExtractor()
research = extractor.extract_article("Subject_Name")
Phase 2: Verify
from scripts.source_verifier import SourceVerifier
verifier = SourceVerifier()
# Verify all citations (DOI, PMID, URL checks)
citation_results = verifier.verify_citations(research['citations'])
# Detect inconsistencies
inconsistencies = verifier.detect_inconsistencies(research)
# Extract Wikipedia uncertainty flags ({{citation needed}}, etc.)
flags = verifier.extract_uncertainty_flags(wikitext)
# Generate verification report
report = verifier.generate_verification_report(
research, citation_results, inconsistencies, flags
)
Phase 3: Enrich
from scripts.entity_extractor import EntityExtractor
entity_extractor = EntityExtractor()
# Extract people, organizations, publications mentioned
entities = entity_extractor.extract_entities(research)
# Map relationships (collaborators, employers, etc.)
relationships = entity_extractor.extract_relationships(
research, entities, "Subject Name"
)
# Build chronological timeline
timeline = entity_extractor.build_timeline(research)
# Generate knowledge graph
graph = entity_extractor.generate_knowledge_graph(
"Subject Name", entities, relationships, timeline
)
Phase 4: Output
from scripts.research_collector import ResearchCollector
collector = ResearchCollector()
collector.save_research({
**research,
'verification': report,
'knowledge_graph': graph
}, "output.json")
Output Schema (Enhanced)
{
"article": {
"title": "Subject Name",
"url": "https://en.wikipedia.org/wiki/...",
"revision_id": "1234567890",
"extracted_at": "2026-02-03T10:30:00Z"
},
"sections": [{
"heading": "Section Name",
"content": "Text content...",
"claims": [{
"text": "Specific factual claim",
"citation_ids": ["ref_1", "ref_2"],
"confidence": 0.92
}]
}],
"citations": [{
"id": "ref_1",
"type": "article-journal",
"title": "Paper Title",
"author": [{"family": "Smith", "given": "John"}],
"DOI": "10.1234/example",
"PMID": "12345678",
"URL": "https://...",
"issued": {"date-parts": [[2024, 1, 15]]}
}],
"verification": {
"verification_summary": {
"total_citations": 15,
"verified_count": 12,
"verification_score": 0.80,
"dead_links": 2,
"archived_recoveries": 1,
"reliability_assessment": "high"
},
"citation_details": {
"ref_1": {
"status": "verified",
"doi_valid": true,
"pmid_valid": true,
"url_accessible": true
}
},
"inconsistencies": [],
"uncertainty_flags": [{
"section": "Early Life",
"type": "citation_needed",
"context": "..."
}]
},
"knowledge_graph": {
"nodes": [
{"id": "Subject", "type": "subject"},
{"id": "Harvard", "type": "organization"},
{"id": "Collaborator Name", "type": "person"}
],
"edges": [
{"source": "Subject", "target": "Harvard", "type": "employment"},
{"source": "Subject", "target": "Collaborator", "type": "collaborator"}
],
"timeline": [
{"date": "2010", "type": "education", "description": "PhD from..."},
{"date": "2016", "type": "award", "description": "Received..."}
]
},
"provenance": {
"source": "Wikipedia",
"extraction_method": "MediaWiki API + wikitext parsing",
"skill_version": "2.0",
"verification_performed": true
},
"metadata": {
"total_citations": 15,
"verified_citations": 12,
"total_claims": 24,
"entities_extracted": 8,
"timeline_events": 6,
"source_quality": {
"rating": "A",
"score": 0.85
}
}
}
Scripts Reference
| Script | Purpose |
|--------|---------|
| wikipedia_client.py | Core API client with caching |
| citation_extractor.py | Extract & parse citations to CSL-JSON |
| research_collector.py | Multi-article research orchestration |
| source_verifier.py | NEW: Verify DOIs, PMIDs, detect dead links |
| entity_extractor.py | NEW: Extract entities, relationships, timelines |
Verification Features
Citation Validation
verifier = SourceVerifier()
result = verifier.verify_citations(citations)
# Each citation gets:
# - status: 'verified', 'accessible', 'dead_link', 'archived'
# - doi_valid: True/False (checked against doi.org)
# - pmid_valid: True/False (checked against PubMed)
# - archive_url: Wayback Machine fallback if dead
Uncertainty Detection
Automatically flags Wikipedia uncertainty templates:
{{citation needed}}- Unsourced claim{{disputed}}- Contested information{{original research}}- May lack sources{{outdated}}- Information may be stale{{who}}/{{when}}- Vague attribution
Inconsistency Detection
Cross-checks claims within the research:
- Date conflicts (PhD year differs between sections)
- Name variations
- Contradictory facts
Entity & Relationship Extraction
Entity Types
| Type | Examples |
|------|----------|
| person | Collaborators, mentors, colleagues |
| organization | Universities, companies, institutes |
| publication_venue | Journals, conferences |
| concept | Research fields, methods |
Relationship Types
| Type | Meaning |
|------|---------|
| collaborator | Research collaboration |
| employment | Work affiliation |
| education | Degree/training |
| publication | Published in venue |
| award_from | Received award from |
Timeline Construction
Automatically extracts chronological events:
{
"timeline": [
{"date": "2005", "type": "education", "description": "BSc from University of Manchester"},
{"date": "2010", "type": "education", "description": "PhD from Humboldt University"},
{"date": "2011", "type": "publication", "description": "Published protein structure paper"},
{"date": "2016", "type": "award", "description": "Received Overton Prize"}
]
}
Quality Metrics
Source Quality (Admiralty Code)
| Rating | Score | Meaning | |--------|-------|---------| | A | 0.80+ | Completely reliable - most citations verified | | B | 0.60-0.79 | Usually reliable | | C | 0.40-0.59 | Fairly reliable | | D | 0.20-0.39 | Not usually reliable | | E | <0.20 | Unreliable |
Confidence Scoring
Method: Additive heuristic based on citation metadata presence.
Each claim's confidence is the average score of its supporting citations, calculated as:
Base score: 0.50
+ DOI present: +0.20 (indicates peer-reviewed)
+ PMID present: +0.15 (indexed in PubMed)
+ ISBN present: +0.10 (published book)
+ URL present: +0.05 (verifiable link)
+ Author info present: +0.10 (attributable)
+ Publication venue named: +0.05 (traceable)
─────────────────────────────────
Maximum possible: 1.00
Typical scores:
| Citation Type | Score | |---------------|-------| | Journal article (DOI + PMID + author) | 0.95-1.0 | | Journal article (DOI + author) | 0.85 | | Book (ISBN + author) | 0.75 | | Webpage (URL + author) | 0.65 | | Bare URL only | 0.55 | | Citation not found | 0.30 |
Limitations of this approach:
- Does NOT verify that the source actually supports the claim
- Does NOT perform semantic analysis of source content
- Assumes DOI ≈ peer-reviewed (not always true for preprints)
- No weighting by journal reputation or citation count
For higher-confidence verification: Use source_verifier.py to validate DOIs/PMIDs exist, then manually verify claim-source alignment for critical facts.
Best Practices
- Always verify - Run source_verifier on all research
- Check uncertainty flags - Wikipedia often marks weak areas
- Build timelines - Chronology reveals inconsistencies
- Extract relationships - Context matters for understanding
- Save revision_id - Wikipedia changes; enable reproducibility
- Use DOIs - Most reliable citation identifiers
- Check archives - Dead links often have Wayback copies
Reference Documentation
references/output_schema.md- Complete JSON schemareferences/api_reference.md- Wikipedia API detailsreferences/citation_templates.md- Parsing guide