Wikidata SPARQL API Guide
SkillDocs & knowledgeQuery Wikidata SPARQL for scholarly metadata, authors, and entities
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Wikidata SPARQL API Guide skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/literature/metadata/wikidata-api-guide/SKILL.md and read by ahel’s review.
Overview
Wikidata is a free, collaborative, multilingual knowledge base maintained by the Wikimedia Foundation. It contains structured data about millions of entities including scholarly articles, academic journals, researchers, universities, and scientific concepts. Each entity has a unique QID and properties linking it to other entities, forming a rich knowledge graph.
For academic researchers, Wikidata serves as a powerful tool for bibliometric analysis, disambiguation of author names, mapping institutional relationships, and linking scholarly outputs across different identifier systems (DOI, ORCID, PubMed ID, arXiv ID, etc.). The SPARQL query service provides a flexible, standards-based interface for complex graph queries.
The Wikidata Query Service is entirely free, requires no authentication, and supports the full SPARQL 1.1 query language. It is especially powerful for cross-referencing scholarly metadata that spans multiple databases and identifier systems.
Authentication
No authentication is required. The Wikidata SPARQL endpoint is free and open.
# No API key needed -- set a descriptive User-Agent header as courtesy
curl -G "https://query.wikidata.org/sparql" \
--data-urlencode "query=SELECT ?item WHERE { ?item wdt:P31 wd:Q5 } LIMIT 5" \
-H "Accept: application/json" \
-H "User-Agent: ResearchClaw/1.0 (academic research tool)"
Core Endpoints
SPARQL Query Endpoint
GET https://query.wikidata.org/sparql?query={SPARQL}&format=json
Parameters:
query(required): URL-encoded SPARQL queryformat: Response format (json,xml,csv,tsv)
Query: Find Papers by a Researcher (via ORCID)
curl -G "https://query.wikidata.org/sparql" \
--data-urlencode 'query=
SELECT ?paper ?paperLabel ?doi WHERE {
?author wdt:P496 "0000-0002-1825-0097" .
?paper wdt:P50 ?author ;
wdt:P356 ?doi .
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
} LIMIT 20' \
-H "Accept: application/json" \
-H "User-Agent: ResearchClaw/1.0"
Query: Journal Impact and Article Counts
SELECT ?journal ?journalLabel ?issn (COUNT(?article) AS ?articleCount) WHERE {
?journal wdt:P31 wd:Q5633421 ;
wdt:P236 ?issn .
?article wdt:P1433 ?journal .
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
}
GROUP BY ?journal ?journalLabel ?issn
ORDER BY DESC(?articleCount)
LIMIT 20
Python Example: Cross-Reference Author Identifiers
import requests
SPARQL_URL = "https://query.wikidata.org/sparql"
HEADERS = {
"Accept": "application/json",
"User-Agent": "ResearchClaw/1.0 (academic research tool)"
}
def query_wikidata(sparql_query):
"""Execute a SPARQL query against Wikidata."""
resp = requests.get(
SPARQL_URL,
params={"query": sparql_query},
headers=HEADERS
)
resp.raise_for_status()
data = resp.json()
return data["results"]["bindings"]
# Find all identifier mappings for a researcher
sparql = """
SELECT ?person ?personLabel ?orcid ?scopus ?dblp ?gscholar WHERE {
?person wdt:P496 "0000-0002-1825-0097" .
OPTIONAL { ?person wdt:P496 ?orcid . }
OPTIONAL { ?person wdt:P1153 ?scopus . }
OPTIONAL { ?person wdt:P2456 ?dblp . }
OPTIONAL { ?person wdt:P1960 ?gscholar . }
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
}
"""
results = query_wikidata(sparql)
for r in results:
print(f"Name: {r.get('personLabel', {}).get('value', 'N/A')}")
print(f" ORCID: {r.get('orcid', {}).get('value', 'N/A')}")
print(f" Scopus: {r.get('scopus', {}).get('value', 'N/A')}")
print(f" DBLP: {r.get('dblp', {}).get('value', 'N/A')}")
print(f" Google Scholar: {r.get('gscholar', {}).get('value', 'N/A')}")
Query: Institutions by Country with Coordinates
SELECT ?uni ?uniLabel ?country ?countryLabel ?coord WHERE {
?uni wdt:P31 wd:Q3918 ;
wdt:P17 ?country ;
wdt:P625 ?coord .
FILTER(?country = wd:Q30)
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
}
LIMIT 50
Common Research Patterns
Author Disambiguation: Use Wikidata to resolve author names by cross-referencing ORCID, Scopus ID, DBLP, and Google Scholar identifiers. This is particularly useful when a common name maps to multiple researchers.
Bibliometric Graph Construction: Build citation and co-authorship networks by querying the relationships between authors, papers, journals, and institutions in the Wikidata graph.
Identifier Translation: Convert between DOI, PubMed ID, arXiv ID, and other identifiers using Wikidata's comprehensive property mappings. This enables linking records across heterogeneous databases.
Institutional Analysis: Map university affiliations, geographic distributions, and organizational hierarchies for researchers in a specific field.
Rate Limits and Best Practices
- Query timeout: 60 seconds; optimize complex queries with filters and limits
- Request rate: No strict published limit, but keep requests under 1 per second for sustained usage
- User-Agent required: Always include a descriptive User-Agent header identifying your application
- LIMIT clause: Always include a LIMIT clause to prevent accidentally fetching millions of results
- Label service: Use
SERVICE wikibase:labelfor human-readable labels instead of QIDs - Caching: Wikidata results are fairly stable; cache results for repeated queries
- Bulk queries: For large-scale data extraction, consider using Wikidata dumps instead of the query service
References
- Wikidata SPARQL Query Service: https://query.wikidata.org/
- Wikidata SPARQL Tutorial: https://www.wikidata.org/wiki/Wikidata:SPARQL_tutorial
- Wikidata Properties for Scholarly Articles: https://www.wikidata.org/wiki/Wikidata:WikiProject_Source_MetaData
- Wikidata REST API: https://www.wikidata.org/wiki/Wikidata:REST_API
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
wikidata-api-guide- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
Related picks
Skill · wshobson
The pick for Pythonpython-pro
Skill · jeffallan
The pick for Pythonagent-browser
Skill · 101-skills
The pick for Data Extractionbright-data-best-practices
Skill · davila7
The pick for Data Extractionfirecrawl-scrape
Skill · firecrawl
The pick for Scrapefirecrawl-build-scrape
Skill · firecrawl
The pick for Scrape