# Wikidata Knowledge Graph & Entity Scraper (`litezero/wikidata-entity-knowledge-scraper`) Actor

Extract structured Wikidata entities, QIDs, labels, descriptions, aliases, and Wikipedia concept URIs via the Wikidata knowledge graph API.

- **URL**: https://apify.com/litezero/wikidata-entity-knowledge-scraper.md
- **Developed by:** [LiteZero](https://apify.com/litezero) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.70 / 1,000 item scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🌐 Wikidata Knowledge Graph & Entity Scraper

Extract structured entities, unique QIDs/PIDs, multilingual labels, semantic descriptions, aliases, and direct knowledge graph URIs from the world's largest open knowledge base: **Wikidata**.

Engineered for entity linking, knowledge graph construction, vector DB enrichment, and AI / LLM grounding.

***

### 🥊 Why This Actor Beats the Competition

| Feature | 🌐 This Actor (`litezero`) | 🐢 Heavy SPARQL Queries | 🔒 Proprietary Knowledge APIs |
|---|---|---|---|
| **Speed** | ⚡ **< 1.0 second / request** | 🐌 30–60s timeouts on complex SPARQL | ⏳ 2–5 seconds with rate limits |
| **Pricing** | 💰 **$1.00 / 1,000 entities** (Free Platform Usage) | 💸 Heavy compute bill ($5–$10/run) | 💳 $0.05–$0.20 per query |
| **Simplicity** | 🚀 Simple keyword search with auto-linking | 🧩 Requires expert SPARQL syntax | 📝 Complex schema transformations |
| **Reliability** | 🛡️ Official Wikimedia API with smart retries | 🚫 Frequent 504 Gateway Timeouts | 🔑 Strict auth tokens & quotas |
| **Output Format** | 📦 Clean, standardized JSON ready for RAG | ⚠️ Verbose RDF XML or raw turtle | 🔒 Vendor lock-in |

***

### 🛠️ Step-by-Step: How to Use

#### Method 1: Apify Web Console (No-Code)

1. Enter your search term (e.g. `artificial intelligence`, `Quantum computing`, `Elon Musk`, or `Python`).
2. Select your language (e.g. `en`, `es`, `de`, `fr`, `ja`).
3. Choose the entity type (`Item` for standard concepts or `Property` for relations).
4. Click **Start** and export results to JSON, CSV, or Excel.

#### Method 2: Python SDK

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run_input = {
    "search": "quantum computing",
    "language": "en",
    "maxResults": 15
}

run = client.actor("litezero/wikidata-entity-knowledge-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item['id']}] {item['label']}: {item['description']}")
```

#### Method 3: Node.js SDK

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('litezero/wikidata-entity-knowledge-scraper').call({
    search: 'machine learning',
    maxResults: 10
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Retrieved ${items.length} entities from Wikidata.`);
```

#### Method 4: REST API / cURL

```bash
curl "https://api.apify.com/v2/acts/litezero~wikidata-entity-knowledge-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"search": "reinforcement learning", "maxResults": 10}'
```

***

### 📥 Input Parameters

| Parameter | Type | Required | Default | Description |
|---|:---:|:---:|:---:|---|
| `search` | `string` | **Yes** | `"artificial intelligence"` | Entity name, person, place, or concept to search. |
| `language` | `string` | No | `"en"` | Language code for labels and descriptions (e.g. `en`, `es`, `fr`). |
| `type` | `string` | No | `"item"` | Entity type: `item` (QID) or `property` (PID). |
| `maxResults` | `integer`| No | `20` | Max entities to return (1–50). |

***

### 📤 Output Data Dictionary & Sample JSON

```json
{
  "id": "Q11660",
  "title": "Q11660",
  "label": "artificial intelligence",
  "description": "intelligence of machines and software",
  "concepturi": "http://www.wikidata.org/entity/Q11660",
  "url": "https://www.wikidata.org/wiki/Q11660",
  "aliases": [
    "AI",
    "machine intelligence",
    "synthetic intelligence"
  ],
  "match_type": "label",
  "match_text": "artificial intelligence",
  "language": "en",
  "scraped_at": "2026-10-04T06:00:00.000Z"
}
```

***

### 💡 Top Commercial Use Cases

1. **Entity Disambiguation & Linking:** Automatically map ambiguous text mentions in unstructured documents to canonical Wikidata QIDs.
2. **Knowledge Graph Generation:** Extract core nodes, properties, and synonym aliases to bootstrap enterprise knowledge graphs.
3. **Semantic RAG Context Injection:** Ground LLM prompts with verified factual summaries and encyclopedic identifiers directly from Wikimedia.

# Actor input Schema

## `search` (type: `string`):

Entity name, concept, or term to search in Wikidata.

## `language` (type: `string`):

ISO language code for entity labels and descriptions (e.g. 'en', 'es', 'de', 'fr').

## `type` (type: `string`):

Wikidata entity type: 'item' (standard Q-item) or 'property' (P-property).

## `maxResults` (type: `integer`):

Maximum number of entities to return (1 to 50).

## Actor input object example

```json
{
  "search": "artificial intelligence",
  "language": "en",
  "type": "item",
  "maxResults": 20
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "search": "artificial intelligence",
    "language": "en",
    "type": "item",
    "maxResults": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("litezero/wikidata-entity-knowledge-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "search": "artificial intelligence",
    "language": "en",
    "type": "item",
    "maxResults": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("litezero/wikidata-entity-knowledge-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "search": "artificial intelligence",
  "language": "en",
  "type": "item",
  "maxResults": 20
}' |
apify call litezero/wikidata-entity-knowledge-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,litezero/wikidata-entity-knowledge-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/xUyxzAgrw8ocEcNz5/builds/dYjFBIFQ0ZMSrTR1v/openapi.json