Wikipedia JSON-LD Structured Data Scraper

Wikipedia JSON-LD Structured Data Scraper

Scrape JSON-LD structured data from any Wikipedia page. Every record returns the full schema.org markup, including article metadata, breadcrumbs, and entity details. Export to CSV, JSON, Excel, or XML.

Wikipedia embeds JSON-LD structured data in every page. This Actor reads the public page source, pulls every JSON-LD block, and flattens it into one fixed schema. You get clean, query-ready rows for articles, organizations, breadcrumbs, and more.

Who uses it	What they scrape Wikipedia for
SEO analysts	Audit Wikipedia's structured data to model schema markup for their own sites
Knowledge graph engineers	Harvest entity relationships and facts from Wikipedia for graph databases
Data journalists	Extract article metadata and citations to track sourcing patterns
Academic researchers	Collect structured bibliographic data from Wikipedia references

What it does

This Actor collects JSON-LD structured data from Wikipedia pages by URL or search term, and returns each block as a flat row with all schema.org properties.

• 🔍 Search or URL input: Feed a Wikipedia search term or a list of page URLs, and the Actor resolves each page automatically.
• 📊 Full schema.org coverage: Extracts all JSON-LD types, including Article, Organization, BreadcrumbList, FAQPage, and more.
• 🧩 Flattened output: Every nested property becomes a dot-notated column, so no data is lost in arrays or objects.
• ⚡ Bulk extraction: Set maxItems up to 1,000,000 for paid users, or preview with 10 items on the free plan.

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

What you can do with Wikipedia data

📈 Audit structured data at scale.

An SEO agency runs the Actor on a list of Wikipedia URLs to compare schema.org usage across article types and inform client markup strategies.

🧠 Build a knowledge graph.

A data engineer extracts entity properties and relationships from Wikipedia pages to populate a Neo4j graph for semantic search.

📰 Track citation networks.

A researcher collects citation and author metadata from Wikipedia articles to analyze how sources are referenced over time.

🤖 Train NLP models.

A machine learning team scrapes article body and description fields from JSON-LD to create a training corpus for text summarization.

Features

	Details
No API key	Uses Wikipedia's public HTML, so you never register an app or wait for OAuth
All JSON-LD types	Captures Article, Organization, BreadcrumbList, FAQPage, and every other schema.org type found on the page
Flat schema	Nested JSON-LD is expanded into dot-notated columns for easy CSV or database import
Bulk ready	Scale from a 10-item preview to a million pages per run on paid plans

Configure the run

Drive the Actor from Wikipedia page URLs or a search term, and set maxItems to control how many pages are processed per run. The Input tab lists every parameter.

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

{
 "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.

Getting started

1. Create a free Apify account.
2. Open the Wikipedia JSON-LD 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-jsonld-scraper"

Then prompt the agent to run the scraper and return the results.

Troubleshooting

Why am I getting no results?

Confirm your start URLs are valid Wikipedia pages that contain JSON-LD blocks. Some pages, like special or category pages, may have limited structured data. Try a search term for a common article.

The Actor returns fewer items than maxItems.

The Actor stops after processing all available pages from your start URLs or search term. If you need more, provide additional URLs or broaden your search term.

Some JSON-LD properties are missing from the output.

Wikipedia pages include different schema.org properties. The Actor extracts whatever is present in the page's JSON-LD. Check the page source to confirm which properties are available.

I get a timeout or 403 error on some pages.

Reduce the number of concurrent requests or add a delay between pages. If the issue persists, try a smaller batch.

FAQ

Question	Answer
Does this Actor require a Wikipedia API key?	It reads the public HTML of Wikipedia pages and extracts embedded JSON-LD directly. No registration or authentication is needed.
What JSON-LD types does it extract?	It extracts all schema.org types present in the page's JSON-LD blocks, including Article, Organization, WebSite, BreadcrumbList, FAQPage, and many others. The output schema includes every property found in the source.
Can I scrape multiple Wikipedia pages in one run?	Provide a list of start URLs or a search term, and set maxItems to the number of pages you want. Free users are limited to 10 items; paid users can go up to 1,000,000.
How is nested JSON-LD handled?	Nested objects and arrays are flattened into dot-notated columns, so properties like author.name or publisher.logo become separate fields in the output.
Does it work with non-English Wikipedia editions?	You can input URLs from any Wikipedia language edition, such as de.wikipedia.org or fr.wikipedia.org, and the Actor will extract the JSON-LD from those pages.
What output formats are supported?	You can export the results as CSV, JSON, Excel, or XML directly from the Apify platform.
Can I filter which JSON-LD blocks are returned?	The Actor returns all JSON-LD blocks found on each page. If you need to filter by type, you can do so after export using the @type field in your dataset.
Is this Actor affected by Wikipedia's rate limits?	The Actor uses polite crawling practices and respects Wikipedia's robots.txt. For very large runs, consider using a higher plan or adding delays between requests.
Does it extract data from Wikipedia tables or infoboxes?	This Actor extracts JSON-LD structured data embedded in the page source. For table extraction, use a dedicated HTML table scraper.
Can I schedule this Actor to run regularly?	You can set up a schedule in Apify to run the Actor daily, weekly, or at any custom interval to keep your structured data up to date.

Related actors

ParseForge collection

🆘 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 JSON-LD Structured Data?

This Actor uses pay-per-result pricing: $0.004 per result collected. You are billed only for the results you receive, so a run that returns nothing costs nothing.