Wikipedia Structured Data Scraper

Wikipedia Structured Data Scraper

Extract structured data (JSON-LD) from Wikipedia pages, up to a million per run. Every page returns its full schema.org metadata - type, name, description, date, author, image, publisher, license, and more. No API key or login required. Export to CSV, JSON, Excel, or XML.

Wikipedia embeds schema.org structured data (JSON-LD) in its HTML pages, but pulling it out manually or via the API is slow and incomplete. This scraper reads those embedded JSON-LD blocks directly from any Wikipedia page, extracting 50+ structured fields per record - from article type and headline to author, publisher, license, potential actions, and interaction statistics. No registration, no rate limits.

Who uses it	What they scrape Wikipedia for
SEO professionals	Audit and enrich Wikipedia entity pages with structured schema data for knowledge graph visibility
Data analysts	Collect author, publisher, date, and license metadata from thousands of Wikipedia articles for compliance reporting
Content researchers	Extract headline, section, and keyword signals from Wikipedia articles to analyze topic coverage
Developers	Feed JSON-LD structured data into knowledge graphs, databases, or content enrichment pipelines

What it does

This Actor extracts embedded JSON-LD structured data from Wikipedia page URLs, returning each page as a flat row with fields like @type, name, headline, description, author, publisher, datePublished, image, video, interactionStatistic, and license.

• 🔍 Start URLs or search term: Provide one or more Wikipedia page URLs, or a search query to discover pages and extract their embedded JSON-LD data.
• 📊 Full schema field extraction: Each page returns its complete structured data block - you get @type, name, description, datePublished, author, publisher, image, license, interactionStatistic, video, and more.
• ⚙️ Limit control: Free users get a 10-page preview. Paid users can set max items up to 1,000,000.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Wikipedia data

🔎 Audit Wikipedia entity pages for structured data completeness.

SEO consultants run this scraper on 1,000+ Wikipedia entity URLs to verify that @type, description, author, and datePublished are present and correctly formatted for knowledge graph appearance.

📦 Build a structured data corpus for NLP training.

Data scientists feed the scraper 10,000 Wikipedia article URLs to extract headline, articleSection, keywords, and description fields for training topic classifiers and entity extractors.

📜 Compile a media license database from Wikipedia.

Media license compliance teams scrape pages of featured articles to collect license, copyrightHolder, copyrightYear, and creator fields for attribution and usage rights reporting.

🔗 Feed Wikipedia metadata into a real-time content enrichment pipeline.

Developers run this scraper daily on a list of monitored Wikipedia article URLs, pushing the JSON-LD output into a database or webhook for automated content updates.

Why choose this scraper

	What you get
Schema completeness	Get 50+ fields per record: @type, name, headline, description, date, author, publisher, image, license, potentialAction, interactionStatistic, video, and more.
No API key needed	The scraper reads public Wikipedia HTML directly - no registration, no API key, no OAuth.
Bulk scale	Run up to 1 million URLs per job, with results streamed to CSV, JSON, Excel, or XML.
Structured by design	Every record follows schema.org JSON-LD, ready for knowledge graphs, enrichment pipelines, or SEO audits.

What a Wikipedia record looks like

Every record returns as one flat JSON row. Here is a real one from a run:

{
 "imageUrl": "https://upload.wikimedia.org/wikipedia/commons/2/2c/Bundesarchiv_DVM_10_Bild-23-61-16%2C_Linienschiff_%22SMS_Wittelsbach%22.jpg?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=original",
 "title": "main page of a Wikimedia project",
 "url": "https://en.wikipedia.org/wiki/Main_Page",
 "type": "Article",
 "@type": "Article",
 "name": "Main Page",
 "sameAs": "http://www.wikidata.org/entity/Q5296",
 "mainEntity": "http://www.wikidata.org/entity/Q5296",
 "datePublished": "2002-01-26T15:28:12Z",
 "dateModified": "2026-06-12T07:37:08Z",
 "image": "https://upload.wikimedia.org/wikipedia/commons/2/2c/Bundesarchiv_DVM_10_Bild-23-61-16%2C_Linienschiff_%22SMS_Wittelsbach%22.jpg?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=original",
 "headline": "main page of a Wikimedia project",
 "sourceUrl": "https://en.wikipedia.org",
 "scrapedAt": "2026-08-19T01:41:26.571Z",
 "author.@type": "Organization"
}

Every value above comes from a real run. A field a record does not have comes back as null.

Configure the run

Drive the Actor from a list of Wikipedia page URLs or a single search term, and the scraper reads each page's embedded JSON-LD structured data, returning only the records that match your input. The Input tab lists every parameter.

A first run with the defaults:

{
 "startUrls": [
 {
 "url": "https://en.wikipedia.org"
 }
 ],
 "maxItems": 10
}

A larger pull:

{
 "startUrls": [
 {
 "url": "https://en.wikipedia.org"
 }
 ],
 "maxItems": 200
}

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the Wikipedia Structured Data Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Wikipedia through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/wikipedia-structured-data-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check that your Start URLs point to actual Wikipedia pages that contain JSON-LD embedded data. Also verify the URL format (e.g., https://en.wikipedia.org/wiki/Article_title). If using a search term, ensure it returns valid results.

Why are some fields missing in my output?

Not every Wikipedia page has a complete JSON-LD block. The scraper returns only the fields that are present in the embedded script. For example, a short stub might lack a 'video' or 'interactionStatistic' field.

The run failed with a 403 error on some pages.

Some Wikipedia category pages or less common subpages return a 403 error. The scraper skips those pages and continues. You can try using different URLs or adding a longer delay between requests.

Can I scrape all pages in a Wikipedia category?

Not directly via the search term field. You can manually compile a list of page URLs within that category and pass them as Start URLs. Alternatively, use Wikipedia's API to get the list first.

FAQ

Question	Answer
Does this work on any Wikipedia language edition?	Yes, the scraper reads embedded JSON-LD from any Wikipedia page, regardless of language. Use the full URL for each language edition (e.g., https://es.wikipedia.org).
What fields does it return?	It returns all schema.org fields present in the embedded JSON-LD block, including @type, name, description, headline, datePublished, dateModified, author, publisher, image, license, interactionStatistic, video, and many more. The exact set varies per page.
Do I need a Wikipedia API key?	No. The scraper reads the public HTML of Wikipedia pages directly - no API key, no login, no OAuth.
How many pages can I scrape per run?	Free users are limited to 10 pages (preview). Paid users can set maxItems up to 1,000,000. There is no per-run limit on the Apify platform beyond that.
Can I scrape a list of non-article pages like categories or user pages?	Yes, as long as the page is a valid Wikipedia page that contains an embedded JSON-LD script tag. Most article, category, and user pages do.
What export formats are supported?	You can export results as CSV, JSON, Excel, XML, or load them into a webhook or database via Apify's integrations.
Is this scraper blocked by CAPTCHAs?	The main Wikipedia page returns 200 OK with content, though some category pages may return 403. If you hit a CAPTCHA, reduce the concurrency or add a small delay between requests.
How do I provide the list of pages to scrape?	You can either paste one or more Wikipedia page URLs directly into the Start URLs field, or provide a search term that the scraper will use to discover matching pages.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Wikimedia Foundation, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

💰 How much does it cost to scrape Wikipedia Structured Data?

This Actor uses pay-per-result pricing: $0.00603 per result collected. You are billed only for the results you receive, so a run that returns nothing costs nothing.