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:

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.

```bash

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

```

Parameters:

Response fields per result:

2. Get Entity Details (wbgetentities)

Retrieve full entity data including claims/identifiers.

```bash

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

```

Parameters:

3. Get Claims Only (wbgetclaims)

Retrieve claims for specific entity/property.

```bash

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:

```bash

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

```

Property search:

```bash

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

```

Optional parameters:

Response fields:

5. Direct Entity JSON (Special:EntityData)

```bash

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

```

`flavor`:

6. Structured Queries (WDQS SPARQL)

```bash

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:

```python

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:

```python

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

```json

{

"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

```json

{

"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`