Article Schema Markup
The technical implementation of schema.org's Article structured-data type on editorial content — marking pages explicitly with title, author, publication date, publisher, image, and body properties so AI engines, Google rich results, and news surfaces can extract the article's metadata cleanly and attribute it to credible source entities.
What is Article Schema Markup?
Article schema is the foundational structured-data type for editorial content — blog posts, news, white papers, guides, opinion pieces, and any long-form content that has identifiable authorship and publication metadata. The schema makes article metadata machine-readable in ways that prose alone cannot: who wrote it, when it was published, when it was updated, who the publisher is, what image represents it, what its primary topic is. AI engines, news aggregators, social previews, and Google rich results all parse Article schema and use the extracted metadata to display, attribute, and rank the content correctly.
Implementation is JSON-LD added to the page head. The Article type includes headline (the article title, ideally matching the H1), datePublished and dateModified properties, an author property pointing to a Person entity, a publisher property pointing to an Organization entity, an image property for the article's primary image, and optionally articleBody for the full text. Subtypes (NewsArticle for news content, BlogPosting for blog posts, TechArticle for technical content) refine the type signal for specific content categories. The visible page should mirror schema fields: the same author, publication date, and headline should appear visibly to readers, not only in structured data.
For AEO, Article schema is the foundational layer that confirms authorship and editorial provenance — both signals AI engines weight as content quality indicators. A page with proper Article schema, paired with a credentialed Person author entity and a recognized Organization publisher entity, presents a complete editorial-provenance chain that engines can evaluate confidently. Pages without Article schema rely on engines to infer authorship and provenance from HTML conventions, which produces less confident attribution. The investment is small (a one-time template-level implementation) but the AEO authority compounding is significant because every editorial page on the site immediately benefits from the consistent structural signal.
Why it matters
Key points about Article Schema Markup
Article schema is the foundational structured-data type for editorial content, declaring authorship, publication metadata, publisher, and other attributes that AI engines weight as quality and provenance signals.
Implementation is JSON-LD in the page head with Article (or subtypes NewsArticle, BlogPosting, TechArticle) declaring headline, dates, author Person entity, publisher Organization entity, image, and optionally articleBody.
Visible content must mirror schema fields — same author, date, and headline appear visibly to readers — to avoid Google's content-mismatch penalty and to maintain reader-engine consistency.
AEO authority compounds because Article schema confirms the editorial-provenance chain (author entity + publisher entity), giving engines confident attribution that anonymous or unstructured pages cannot match.
Investment is one-time template-level work; every editorial page on the site immediately benefits from the consistent structural signal, compounding authority across the entire content library.
Frequently asked questions about Article Schema Markup
What is Article schema markup and when should I use it?
What properties should Article schema include at minimum?
How does Article schema improve AEO performance?
Related terms
Author profile content — usually placed on each content asset and on a dedicated author page — that establishes the writer's credentials, experience, and authority in the subject area, with structured-data confirmation via Person schema and sameAs links to authoritative external profiles such as LinkedIn, university affiliations, or industry registries.
Read definition → JSON-LD (Linked Data)JSON-LD (JavaScript Object Notation for Linked Data) is the preferred format for embedding structured data on web pages — a script block in the page head or body that describes entities, attributes, and relationships in a machine-readable way, enabling AI engines and search systems to parse content with precision rather than inference.
Read definition → Schema.org MarkupMachine-readable structured data annotations, typically implemented via JSON-LD, that explicitly describe the entities, relationships, and attributes on a webpage so that search engines and AI systems can parse content with precision rather than inference.
Read definition → Structured DataA standardized way of labeling page information so search engines, AI systems, and knowledge graphs can understand entities, attributes, relationships, and content purpose with less ambiguity.
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