Wikipedia Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping. Get started quickly with our Getting started guide.

TL;DR

To get structured Wikipedia data via API, define a JSON schema for the fields you need (title, summary, categories, etc.), then call AlterLab's Extract API with the target URL and schema. You'll receive validated, typed JSON output—no HTML parsing, regex, or brittle selectors required.

Why use Wikipedia data?

Wikipedia is the largest free knowledge base, making it invaluable for:

• AI training data: Extract clean, structured reference text for fine-tuning LLMs on factual knowledge
• Analytics pipelines: Track concept popularity over time by monitoring category changes or infobox updates
• Reference enrichment: Augment internal knowledge graphs with standardized titles, descriptions, and multilingual links

What data can you extract?

From any public Wikipedia page, you can extract:

• Core metadata: title (string), url (string), last_modified (date)
• Content: summary (string, lead section), full_text (string, entire article)
• Taxonomy: categories (array of strings), languages (object mapping language codes to titles)
• Structured facts: Infobox values (e.g., population for country pages, founded_year for organizations)

All fields are defined by your schema—AlterLab returns only what you request, properly typed.

The extraction approach

Raw HTTP requests plus HTML parsing fail on Wikipedia due to:

• Dynamic content loaded via JavaScript (infoboxes, interactive elements)
• Frequent DOM changes breaking CSS selectors
• Rate limits and bot detection on direct API calls
• Encoding complexities in multilingual content

AlterLab's Extract API solves this by combining:

1. Automated browser rendering (handles JS, lazy loading)
2. AI-powered semantic understanding (identifies content blocks regardless of DOM structure)
3. Schema validation (output matches your JSON exactly—no cleanup needed)
4. Built-in rate limiting and proxy rotation (stays within Wikipedia's TOS)

Quick start with AlterLab Extract API

Here's how to extract title, summary, and categories from a Python script:

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "Article title from <h1>"
    },
    "summary": {
      "type": "string",
      "description": "First paragraph before table of contents"
    },
    "categories": {
      "type": "array",
      "items": {"type": "string"},
      "description": "List of category names from page footer"
    }
  }
}

result = client.extract(
    url="https://en.wikipedia.org/wiki/Artificial_intelligence",
    schema=schema,
)
print(result.data)

For quick testing, use cURL:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://en.wikipedia.org/wiki/Artificial_intelligence",
    "schema": {
      "properties": {
        "title": {"type": "string"},
        "summary": {"type": "string"}
      }
    }
  }'

Both examples return structured JSON like:

{
  "title": "Artificial intelligence",
  "summary": "Artificial intelligence (AI) is intelligence demonstrated by machines...",
  "categories": [
    "Artificial intelligence",
    "Computer science",
    "Cognitive science"
  ]
}

See the Extract API docs for full parameter details and response codes.

Define your schema

Your JSON schema drives the extraction:

• Type safety: AlterLab validates output against your schema (e.g., ensures categories is always an array)
• Field descriptions: Help the AI understand context (e.g., "description": "Infobox value for 'founded'")
• Nested objects: Extract structured infoboxes:
  JSON

  Copy

  "infobox": {
    "type": "object",
    "properties": {
      "founded": {"type": "string", "format": "date"},
      "founders": {"type": "array", "items": {"type": "string"}}
    }
  }

• Optional fields: Add "required": [] to skip missing data without errors

AlterLab returns null for undefined fields and strips extra properties—giving you clean, predictable output every time.

Handle pagination and scale

For bulk extraction (e.g., all pages in a category):

1. Batching: Process 50-100 URLs per request using AlterLab's job API
2. Rate limiting: Stay under 1 req/sec for Wikipedia (adjust delay parameter in batch jobs)
3. Async workflow:
   Python

   Copy

   job = client.create_batch_job(
       urls=["https://en.wikipedia.org/wiki/AI", "https://en.wikipedia.org/wiki/ML"],
       schema=schema,
       concurrency=5
   )
   results = client.wait_for_job(job.id)

4. Cost efficiency: Pay only for successful extractions—no charges for failed requests or retries. Volume pricing starts at 10k requests/month; see pricing for details.

Monitor usage via the dashboard to optimize costs—alterlab.io bills per successful extraction, not compute time.

Key takeaways

• Schema-first: Define your data structure upfront—AlterLab handles the complexity of turning HTML into validated JSON
• Public data only: Stick to openly visible content; respect robots.txt and rate limits (AlterLab enforces compliant access)
• Zero maintenance: No selector updates when Wikipedia changes its layout—AI adapts automatically
• Production-ready: Output is typed, sanitized, and ready for direct insertion into databases or ML pipelines

Start extracting structured Wikipedia data today—your schema is the only API documentation you need.