📚 Wikipedia Scraper — Bulk Articles, Infoboxes & Structured Knowledge at Scale

Extract clean article text, infoboxes, references, categories, and structured knowledge from Wikipedia — for any language edition, by article title or category. A drop-in alternative to the rate-limited Wikipedia REST API, DBpedia dumps, the Wikidata SPARQL endpoint, and DBpedia Spotlight — without throttle limits or downloading multi-GB dumps.

Why This Scraper Beats Wikipedia API, DBpedia, Wikidata SPARQL & DBpedia Spotlight

Feature	NexGenData Wikipedia Scraper	Wikipedia REST API	DBpedia	Wikidata SPARQL	DBpedia Spotlight
Cost	$3 per 1,000 articles, pay-per-event	Free, rate-limited (200 req / sec global)	Free (dump + run)	Free, query-cap	Free (self-host)
Bulk export	Unlimited CSV / JSON / Excel	Per-article round-trip	Multi-GB RDF dump	SPARQL queries	NLP only
Output	Clean structured JSON	HTML / Wikitext	RDF / N-Triples	JSON / XML	Entity links
Languages	300+ Wikipedia editions	300+	100+	Multilingual	English-heavy
Auth	Apify token	None (anonymous)	None	None	Self-hosted
Time-to-first-row	< 60 seconds	None, but rate-limited	Hours of dump processing	SPARQL learning curve	Server setup
Infobox parsing	Yes (structured key-value)	Wikitext only	Yes	Yes	None
Schedule + webhook	Native	None	None	None	None

Most teams pick this scraper because it is the only turnkey way to bulk-export 10,000 Wikipedia articles + their infoboxes + references into a CSV — without learning SPARQL, downloading a 100GB DBpedia dump, or hitting Wikipedia's anonymous rate limit.

What You Get

For each article:

• Title + canonical Wikipedia URL
• Lead paragraph (clean text, no wikitext markup)
• Full body sectioned by H2 / H3 heading
• Infobox parsed as structured key-value JSON (population, founded, CEO, etc.)
• Summary (first 2-3 sentences for RAG / embeddings)
• Categories (full category tree)
• References / external links with source URLs
• Images — main image + caption + URL
• Wikidata ID for cross-reference to the global knowledge graph
• Pageviews (last 30 days, where available)
• Last edited timestamp + editor count
• Language + interwiki links to other Wikipedia editions

Output is clean JSON ready for RAG indexing, LLM context-packing, or warehouse ETL.

Use Cases

• RAG / AI training data — bulk-pull entity articles for grounding an LLM
• Entity enrichment — augment a company / person / city dataset with Wikipedia infobox fields
• Academic research — measure article-edit frequency vs real-world news cycles
• Knowledge-graph construction — combine with Wikidata IDs for entity-linking pipelines
• SEO research — surface which Wikipedia articles link to your target site
• Educational products — build flashcards or trivia content from category trees
• Fact-checking pipelines — pull authoritative-ish baseline for LLM hallucination detection
• Language-modeling research — multi-language parallel article extraction

Quick Start

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/wikipedia-scraper").call(run_input={
    "titles": ["Albert Einstein", "Apify", "Apify Platform"],
    "language": "en",
    "extractInfobox": True,
    "extractReferences": True
})
for article in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(article["title"], article["wikidata_id"], len(article["body"]))

Pricing

Pay-per-event — no monthly minimum:

• Actor Start: ~$0.0002 per run
• Result: $0.003 per article scraped

Examples:

• 100 articles ≈ $0.30
• 1,000 articles ≈ $3
• 10,000 articles ≈ $30
• A 50K-article RAG corpus ≈ $150 one-time

Related NexGenData Actors

Use case	Actor
Google Scholar bulk paper + citation scraper	Google Scholar Scraper
arXiv keyword search + bulk paper export	arXiv Scraper
Generic full-site crawler for AI / RAG	Website Content Crawler
AI-callable academic research for agents	Academic Research MCP Server
News + press release monitoring for agents	News MCP Server
Hacker News stories + comments scraper	Hacker News Scraper
Reddit subreddit + post trend tracking	Reddit Subreddit Trends
AI sentiment analysis for any text	AI Sentiment Analyzer

FAQ

Q: Which Wikipedia languages are supported? All 300+ editions. Pass language (ISO 639-1, e.g. en, fr, de, zh) plus the article title in that language.

Q: How fresh is the data? Live — each request hits Wikipedia in real time. For a snapshot in time, capture the lastEdited timestamp.

Q: Is the infobox always parsed? For articles with a standard infobox template, yes. Some niche articles use custom infobox layouts that fall back to raw key-value extraction.

Q: Can I get Wikidata IDs? Yes — every result includes the linked wikidata_id (Q-number) for cross-referencing the global knowledge graph.

Q: Is the output safe for redistribution? Wikipedia content is CC-BY-SA 4.0. Cite Wikipedia + the contributing editors per the license terms.

Q: How does this compare to the Wikipedia REST API? The REST API is excellent for one-off lookups but rejects bulk patterns (200 req/sec global throttle + UA enforcement). This actor multiplexes through Apify-managed proxies for sustained bulk extraction.

Q: Can I scrape by category instead of title? Yes — pass a category and the actor walks the category members.

About NexGenData

NexGenData publishes 260+ buyer-intent actors covering SEC filings, YC alumni, academic research, lead generation, competitive intelligence, stock fundamentals across 30+ exchanges, and MCP servers for AI agents. All pay-per-result. Browse the full catalog at https://apify.com/nexgendata?fpr=2ayu9b

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How NexGenData Pricing Works

Every NexGenData actor uses pay-per-event pricing — you only pay for results that actually land in your dataset. No monthly minimum, no seat fees, no surprise overage bills.

• Actor Start: a single-event charge each time you spin the actor up (scaled to memory size)
• Result: charged per item written to the default dataset
• No charge for retries, internal proxy rotation, or failed sub-requests — those are absorbed by the platform

If you only need the data once a quarter, you only pay once a quarter. If you scale to millions of records, the unit cost stays the same.

Apify Platform Bonus

New to Apify? Sign up with the NexGenData referral link — you get free platform credits on signup (enough for several thousand free results) and you help fund the maintenance of this actor fleet.

Integration Surface

Every actor in the NexGenData catalog can be triggered from:

• Apify console — point-and-click run
• Apify API — REST + webhooks
• Apify Python / JS SDKs — programmatic batch
• Zapier, Make.com, n8n — official integrations
• MCP — many actors are exposed as MCP tools for Claude / ChatGPT / Cursor agents
• Schedules — built-in cron for daily / weekly / monthly runs
• Webhooks — POST results to any HTTPS endpoint on dataset write

Support

NexGenData maintains 260+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get a response within 24 hours. Roadmap requests are welcome — high-demand features ship in the next version.

🏠 Home: thenextgennexus.com 📦 Full catalog: apify.com/nexgendata