Wikidata Search Skill

Search and retrieve data from Wikidata, the free knowledge base.

Critical: Things Claude Won't Know Without This Skill

Wikidata Vector Database (semantic search)

This is the highest-value feature of this skill. The Wikidata Vector Database at wd-vectordb.wmcloud.org provides semantic/hybrid search over all Wikidata items — something you can't do with the standard Action API or SPARQL.

A descriptive User-Agent header is required or you get 403.

curl -H 'User-Agent: WikidataSearchSkill/1.0 (contact: you@example.com)' \
  'https://wd-vectordb.wmcloud.org/item/query/?query=historical+Chinese+cartography&lang=all&K=20'

Response includes QID, similarity_score, rrf_score, and source (vector vs keyword).

Property search: replace /item/query/ with /property/query/.

Optional params: lang, K (result count), instanceof (comma-separated QIDs), rerank.

WDQS SPARQL also requires User-Agent

curl -G 'https://query.wikidata.org/sparql' \
  --data-urlencode 'query=SELECT ?item ?label WHERE { ?item wdt:P31 wd:Q12857432 . ?item rdfs:label ?label . FILTER(LANG(?label)="en") }' \
  -H 'Accept: application/sparql-results+json' \
  -H 'User-Agent: WikidataSearchSkill/1.0 (contact: you@example.com)'

External identifiers live in claims

# claims[property_id][0]["mainsnak"]["datavalue"]["value"] → identifier string
# Common: P214 (VIAF), P244 (LoC), P227 (GND), P213 (ISNI), P268 (BnF)

Choosing an Access Method

Need	Method
Keyword search by label/alias	Action API wbsearchentities
Semantic / fuzzy concept discovery	Vector Database (hybrid vector + keyword)
Fetch a known entity's JSON	Special:EntityData/{ID}.json
Complex graph queries / reporting	WDQS SPARQL

Python Script

Use scripts/wikidata_api.py for programmatic access (zero dependencies):

from scripts.wikidata_api import WikidataAPI
wd = WikidataAPI()

# Keyword search
results = wd.search("Zhu Xi", language="en", limit=5)

# Semantic search (Vector DB) — the key differentiator
candidates = wd.vector_search_items("historical Chinese cartography", lang="all", k=20)

# Entity retrieval
entity = wd.get_entity("Q9397", props=["labels", "descriptions", "claims"])

# External identifiers
ids = wd.get_identifiers("Q9397", include_labels=True)
# → {'VIAF ID (P214)': '46768804', 'Library of Congress ID (P244)': 'n81008179', ...}

# SPARQL
results = wd.sparql_json("SELECT ?item ?label WHERE { ?item wdt:P31 wd:Q12857432 . ?item rdfs:label ?label . FILTER(LANG(?label)='en') }")

# Direct entity JSON (fast for current state)
data = wd.get_entitydata("Q42", flavor="simple")

API Endpoints Quick Reference

Endpoint	URL
Action API	https://www.wikidata.org/w/api.php
Entity JSON	https://www.wikidata.org/wiki/Special:EntityData/{ID}.json
SPARQL	https://query.wikidata.org/sparql
Vector DB	https://wd-vectordb.wmcloud.org

API Etiquette

• Rate limit: 0.5–1s between requests
• User-Agent: Required for Vector DB and WDQS (include contact info)
• Respect 429: Honor Retry-After headers
• Action API: Use maxlag parameter; batch with pipe-separated IDs (max 50)
• SPARQL: Request only needed fields; use LIMIT

Related Skills

• cbdb-api: Cross-reference Wikidata entities with CBDB biographical data for Chinese historical figures
• chgis-tgaz: Look up historical places found via Wikidata in the CHGIS Temporal Gazetteer for detailed administrative history

Resources

• references/api_reference.md — Complete API specs for all four access methods
• scripts/wikidata_api.py — Full-featured Python client with rate limiting, retries, and identifier extraction