01
Articles and infoboxes
Turn articles and infoboxes into structured inputs for knowledge graph enrichment; preserve article, section, revision, citation, and collection-time context.
All Data API targets
Data API•Managed extraction
Wikipedia Scraper API
Transform Wikipedia articles, infoboxes, categories, references, and revisions into traceable knowledge and entity data.
Structured output · Source-aware fields · Parser maintenance included
Available data
Build knowledge graph enrichment from Wikipedia source views.
Build a source-linked view of page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values from articles and infoboxes, category, list, and disambiguation pages, and revision histories and reference sections for knowledge graph enrichment. Turn linked entities, categories, infobox facts, and redirects into connected records for search and discovery.
02
Category, list, and disambiguation pages
Use category, list, and disambiguation pages to give research and citation discovery a source-linked view with article, section, revision, citation, and collection-time context.
03
Revision histories and reference sections
Build knowledge change monitoring from revision histories and reference sections, carrying article, section, revision, citation, and collection-time context into delivery.
Business use cases
Wikipedia data for knowledge graph enrichment, research and citation discovery, and more.
Use page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values as repeatable inputs for knowledge graph enrichment, research and citation discovery, and knowledge change monitoring. Surface the references behind an article so researchers can move quickly from a topic overview to supporting sources.
Structured output
Wikipedia fields shaped for knowledge graph enrichment.
Keep page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values connected to article, section, revision, citation, and collection-time context in a consistent, application-ready record. Track revisions, citations, and structural changes across important pages to keep knowledge products current.
01
Page ID, title, and canonical URL
Use page ID, title, and canonical URL as the stable key for Wikipedia records across refreshes and connected systems.
02
Lead summary and section hierarchy
Use lead summary and section hierarchy as a source-linked text input for Wikipedia classification and comparison workflows.
03
Infobox attributes and values
Keep infobox attributes and values connected to its offer, market, and capture context for comparable Wikipedia data.
04
Categories and page relationships
Keep categories and page relationships connected to its Wikipedia source view and collection context.
05
Linked entities and redirects
Use linked entities and redirects as a structured input for research and citation discovery.
06
Citation titles, URLs, and publishers
Capture citation titles, URLs, and publishers with conversation and collection context for Wikipedia evaluation and monitoring.
07
Revision ID, editor, and timestamp
Use revision ID, editor, and timestamp as the stable key for Wikipedia records across refreshes and connected systems.
08
Language and page-status context
Capture language and page-status context in a consistent schema for Wikipedia stock, listing, or result-state monitoring.
How it works
Turn category, list, and disambiguation pages into data for research and citation discovery.
WebScrapingAPI turns category, list, and disambiguation pages into page ID, title, and canonical URL and lead summary and section hierarchy records while maintaining collection and parsers.
- 01
Choose source views
Start with articles and infoboxes, category, list, and disambiguation pages, and revision histories and reference sections, then choose the articles, topics, revisions, and reference views required for knowledge graph enrichment.
- 02
Select data fields
Focus the output on Page ID, title, and canonical URL, Lead summary and section hierarchy, and the additional context your application uses.
- 03
Receive structured records
Send page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values to knowledge, retrieval, and research systems, with article, section, revision, citation, and collection-time context attached.
- 04
Scale with managed quality
WebScrapingAPI maintains access, extraction logic, parsers, and product-level monitoring as your workload grows.
Managed maintenance
Keep Wikipedia extraction for research and citation discovery off your engineering backlog.
We maintain category, list, and disambiguation pages and revision histories and reference sections, map page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values, and monitor record quality for knowledge change monitoring.
Your team receives page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values ready for knowledge graph enrichment.
WebScrapingAPI
Managed Wikipedia collection
- Articles and infoboxes access
- Page ID, title, and canonical URL and Infobox attributes and values mapping
- Parser maintenance for category, list, and disambiguation pages
- Quality monitoring for knowledge graph enrichment
Ready for your team
Wikipedia data built to move
- Apply Page ID, title, and canonical URL and Infobox attributes and values to research and citation discovery
- Connect citation titles, URLs, and publishers to your applications
- Build research and citation discovery into dashboards, models, or alerts
- Scale knowledge change monitoring by volume and refresh cadence
Get started
See Wikipedia records shaped around knowledge change monitoring.
Start with category, list, and disambiguation pages and revision histories and reference sections and see page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values in structured output for research and citation discovery.
Sample data
Start with representative Wikipedia records.
Choose articles and infoboxes and see page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values in structured output shaped for knowledge graph enrichment.
Talk to a data expertFAQ
Wikipedia Scraper API FAQs.
Learn what data is available, how maintenance works, and how to get started.
What does the Wikipedia Scraper API cover?
Wikipedia Scraper API turns articles and infoboxes, category, list, and disambiguation pages, and revision histories and reference sections into structured records for knowledge graph enrichment, research and citation discovery, and knowledge change monitoring.
Which Wikipedia pages or entities can I collect?
Choose from Articles and infoboxes, Category, list, and disambiguation pages, and Revision histories and reference sections, then select the articles, topics, revisions, and reference views and fields required for knowledge graph enrichment.
Which fields can Wikipedia records contain?
Available field families include Page ID, title, and canonical URL, Lead summary and section hierarchy, Infobox attributes and values, Categories and page relationships, Linked entities and redirects, and Citation titles, URLs, and publishers. Request a sample shaped around page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values.
How do I start collecting Wikipedia data?
Start free or request sample Wikipedia data. Our team can help you choose the source views, fields, and delivery option that fit your workflow.
Who maintains Wikipedia source access and parsing?
WebScrapingAPI handles Wikipedia source access, extraction, parser maintenance, and product-level quality monitoring so your team can focus on knowledge graph enrichment.
How can Wikipedia data fit my existing workflow?
Send page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values to knowledge, retrieval, and research systems. Start with sample Wikipedia data, then scale volume and refresh cadence as your workflow grows.
How is Wikipedia Scraper API priced?
Pricing depends on source, volume, frequency, fields, and delivery method. Start free for an initial test or talk to a data expert for a plan matched to your workload.
Your Wikipedia data
Make Wikipedia records part of research and citation discovery.
Start with research and citation discovery, or request sample Wikipedia records built around page ID, title, and canonical URL, lead summary and section hierarchy, and infobox attributes and values for knowledge change monitoring.