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:

| Property | Name | Example | | -------- | ---------------------- | ---------------------- | | P214 | VIAF ID | 75121530 | | P227 | GND ID | 119033364 | | P244 | Library of Congress ID | n79023811 | | P213 | ISNI | 0000 0001 2144 9326 | | P345 | IMDb ID | nm0001354 | | P646 | Freebase ID | /m/0282x | | P349 | NDL ID | 00621256 | | P268 | BnF ID | 11888092r | | P269 | IdRef ID | 026927608 | | P906 | SELIBR ID | 182099 | | P396 | SBN author ID | IT\ICCU\CFIV\000163 |

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

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