Wikidata Search Skill

Wikidata Search Skill

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

Choosing An Access Method

Use the method that matches the task to reduce load and improve accuracy:

• Keyword search by label/​alias/​description: Action API wbsearchentities
• Semantic exploration / fuzzy concept search: Wikidata Vector Database (hybrid vector + keyword via RRF)
• Fetch a known entity's current JSON quickly: Special:EntityData
• Complex graph relations / reporting: Wikidata Query Service (WDQS) SPARQL

API Endpoints

Base URL: https://www.wikidata.org/​w/​api.php

Entity JSON (often faster for current state): https://www.wikidata.org/​wiki/​Special:EntityData/​{ID}.json

SPARQL endpoint: https://query.wikidata.org/​sparql

Vector DB API: https://wd-vectordb.wmcloud.org

Core Functions

1. Search Items (wbsearchentities)

Search for entities by label or alias.

curl 'https://www.wikidata.org/​w/​api.php?action=wbsearchentities&search=QUERY&language=en&format=json&type=item&limit=10'

Parameters:

• search: Search term (required)
• language: Language code (default: en)
• type: item (Q-entities) or property (P-entities)
• limit: Max results (1-50, default: 7)
• continue: Offset for pagination

Response fields per result:

• id: Entity ID (e.g., Q42)
• label: Primary label
• description: Short description
• aliases: Alternative names
• url: Wikidata page URL

2. Get Entity Details (wbgetentities)

Retrieve full entity data including claims/​identifiers.

curl 'https://www.wikidata.org/​w/​api.php?action=wbgetentities&ids=Q42&format=json&props=labels|descriptions|aliases|claims'

Parameters:

• ids: Pipe-separated entity IDs (max 50)
• props: labels|descriptions|aliases|claims|sitelinks|info
• languages: Filter languages (e.g., en|fr|de)

3. Get Claims Only (wbgetclaims)

Retrieve claims for specific entity/​property.

curl 'https://www.wikidata.org/​w/​api.php?action=wbgetclaims&entity=Q42&property=P31&format=json'

4. Semantic / Hybrid Search (Wikidata Vector Database)

When you don't know the exact label, or want "things like this" discovery, use the Vector DB.

Item search:

curl 'https://wd-vectordb.wmcloud.org/​item/​query/?query=QUERY&lang=all&K=20'

Property search:

curl 'https://wd-vectordb.wmcloud.org/​property/​query/?query=QUERY&lang=all&K=20&exclude_external_ids=false'

Optional parameters:

• lang: language code, or all for cross-language
• K: number of results
• instanceof: comma-separated QIDs to filter items by "instance of"
• rerank: true|false (slower)

Response fields:

• QID / PID
• similarity_score
• rrf_score
• source

5. Direct Entity JSON (Special:EntityData)

curl 'https://www.wikidata.org/​wiki/​Special:EntityData/​Q42.json?flavor=simple'

flavor:

• simple: truthy statements + sitelinks/​version
• full: full data

6. Structured Queries (WDQS SPARQL)

curl -G 'https://query.wikidata.org/​sparql' --data-urlencode 'query=SELECT * WHERE { wd:Q42 ?p ?o } LIMIT 5' -H 'Accept: application/​sparql-results+json'

Extracting External Identifiers

External identifiers are stored as claims with datatype external-id. Common identifier properties:

To extract identifiers from wbgetentities response:

# claims = response['entities']['Q42']['claims']
# For each property P:
#   claims[P][0]['mainsnak']['datavalue']['value'] -> identifier string

Python Script Usage

Use scripts/​wikidata_api.py for programmatic access:

from scripts.wikidata_api import WikidataAPI

wd = WikidataAPI()

# Search for items
results = wd.search("Albert Einstein", language="en", limit=5)

# Get entity with identifiers
entity = wd.get_entity("Q937", props=["labels", "descriptions", "claims"])

# Get external identifiers only (all values by default)
identifiers = wd.get_identifiers("Q937")
# Returns: {'P214': ['75121530', ...], 'P227': '118529579', ...}

# Semantic search (Vector DB)
candidates = wd.vector_search_items("a famous science fiction writer", lang="en", k=5)

# SPARQL
raw = wd.execute_sparql("SELECT * WHERE { wd:Q42 ?p ?o } LIMIT 5")

Response Handling

Search Response Structure

{
  "searchinfo": {"search": "query"},
  "search": [
    {
      "id": "Q42",
      "label": "Douglas Adams",
      "description": "English writer and humorist",
      "aliases": ["Douglas Noël Adams"],
      "url": "//www.wikidata.org/​wiki/​Q42"
    }
  ]
}

Entity Response Structure

{
  "entities": {
    "Q42": {
      "type": "item",
      "id": "Q42",
      "labels": {"en": {"language": "en", "value": "Douglas Adams"}},
      "descriptions": {"en": {"language": "en", "value": "..."}},
      "claims": {
        "P31": [...],  // instance of
        "P214": [{"mainsnak": {"datavalue": {"value": "113230702"}}}]  // VIAF
      }
    }
  }
}

Best Practices

1. Choose the right access method: search vs vector search vs entity fetch vs SPARQL
2. Rate limiting: add 500ms-1s delay between requests
3. Batch requests: use pipe-separated IDs (max 50 per wbgetentities call)
4. Set User-Agent: include contact info in headers
5. Handle 429: respect Retry-After and back off
6. Action API etiquette: use maxlag and request only needed props