Wikidata Search
Search Wikidata items, properties, and entities with keyword, semantic, and SPARQL queries
What You Can Do
You can search Wikidata using four complementary methods: keyword search via the Action API for label-based lookups, semantic/hybrid search through the Vector Database for fuzzy concept matching, direct entity retrieval via Special:EntityData for instant JSON data, and SPARQL queries through WDQS for complex graph relationships and reporting. Each method is optimized for different use cases—from quick entity lookups to multi-table data analysis across millions of linked facts.
Features
Example Output
Keyword Search Result:
Query: "Albert Einstein"
Result: Q937 (Albert Einstein)
- Label: Albert Einstein
- Description: German-born theoretical physicist
- Aliases: Einstein, A. Einstein, Alfred Einstein
- URL: https://www.wikidata.org/wiki/Q937
Entity Details Retrieval:
Entity: Q937 (Albert Einstein)
- Birth Date: 1879-03-14
- Death Date: 1955-04-18
- Occupation: Theoretical physicist (Q61002)
- Awards: Nobel Prize in Physics (Q14847)
- Wikipedia: en.wikipedia.org/wiki/Albert_Einstein
SPARQL Query Result:
All physicists who won the Nobel Prize:
| Person | Birth Year | Award |
|--------|-----------|-------|
| Albert Einstein | 1879 | Nobel Prize in Physics |
| Marie Curie | 1867 | Nobel Prize in Physics |
What's Included
- SKILL.md: Core instruction file with all four API methods and endpoint documentation
- Search Templates: Pre-built keyword search, semantic search, and entity lookup queries
- SPARQL Query Examples: Sample SPARQL patterns for common graph queries (relationships, awards, dates)
- Response Parsing Guide: How to extract claims, labels, descriptions, and external identifiers from JSON
- Method Selection Checklist: Decision tree for choosing the right access method based on your task
Who It's For
- Data analysts and researchers building knowledge graphs or fact-checking systems
- Semantic web developers integrating linked data into applications
- Information architects designing entity relationship databases
- Academic researchers harvesting structured metadata across domains
- Content creators and editors verifying facts against authoritative sources
Best For
- Finding and validating entity IDs and metadata quickly
- Building datasets from Wikidata's structured claims and relationships
- Powulating knowledge bases with verified external identifiers (ISBN, ORCID, etc.)
- Complex multi-step graph queries and relationship discovery
- Fuzzy concept matching and semantic exploration across domains
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