Article is a Schema.org type used to markup a news, scholarly, or blog article, providing signals about the headline, author, datePublished, and publisher to help search engines understand content authorship and topical authority. It is a core component of Generative Engine Optimization, enabling platforms like Google to accurately attribute content to a verified entity and display rich results such as top stories or author bylines. Proper implementation requires nesting properties like Organization and Person to establish a machine-readable provenance chain.
Glossary
Article

What is Article?
The Article schema is a foundational structured data vocabulary for defining a self-contained written composition on a webpage, providing explicit signals about authorship, publication date, and publisher identity to search engines.
Distinct from BlogPosting or NewsArticle, the generic Article type serves as the parent class for more specific content subtypes. Critical properties include mainEntityOfPage to define the primary topic, dateModified to signal content freshness, and author to link to a verified Person or Organization entity. When combined with Entity Reconciliation via the sameAs property, the Article schema becomes a powerful signal for establishing Algorithmic Trust, directly feeding knowledge graph grounding and Citation Integrity Scoring mechanisms.
Core Properties of Article Markup
The essential Schema.org properties that define an Article entity, communicating authorship, temporal context, and publisher authority to search engines for optimal content indexing and rich result eligibility.
headline
The title of the article as it should appear in search results and rich snippets. This is a required property that must exactly match the visible page title or provide a more concise version for search engines. Google uses this to generate the clickable link in search results and as a primary signal for topical relevance.
- Must be a single text string, not HTML
- Should be between 50-70 characters for optimal display
- Differs from
namewhich is the full title including subtitles
author
Defines the creator of the article content, typically linked to a Person or Organization entity. This property is critical for establishing E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) by connecting content to a verified author entity with its own structured data.
- Expects a Person or Organization type
- Should include author.name and author.url for disambiguation
- Multiple authors can be specified using an array
- Links content to author's knowledge graph entity
datePublished
The original publication date of the article in ISO 8601 format. This temporal signal helps search engines determine content freshness and chronological relevance for time-sensitive queries. It is a required property for eligibility in the Top Stories carousel and other news-related rich results.
- Format:
YYYY-MM-DDorYYYY-MM-DDThh:mm:ss+TZD - Must reflect the date the content first became publicly accessible
- Works in conjunction with
dateModifiedto signal updates
dateModified
Indicates the most recent date and time the article was substantively updated. Search engines use this property to distinguish between stale content and actively maintained resources, which can influence ranking for queries where freshness is a factor.
- Only set when meaningful editorial changes occur
- Do not update for minor typo fixes or ad changes
- Combined with
datePublished, provides a complete temporal history - Critical for content that requires ongoing accuracy like technical documentation
publisher
Defines the Organization responsible for publishing the article. This property connects the content to a verified publisher entity in the knowledge graph, consolidating authority signals at the organizational level. The publisher should be an Organization type with its own structured data including logo and official name.
- Typically references the site's parent organization
- Must include publisher.name and publisher.logo for Article rich results
- Distinct from
authorwhich is the individual creator - Logo must be a valid ImageObject with specific dimension requirements
mainEntityOfPage
Explicitly identifies the canonical URL of the webpage that primarily contains this article. This property resolves ambiguity when an article entity is embedded in a page with multiple entities, telling search engines that this article is the primary subject of the page rather than supplementary content.
- Should contain the canonical URL as a single string or a WebPage object with @id
- Prevents entity confusion on pages with multiple structured data types
- Strengthens the association between the URL and the article entity
Frequently Asked Questions
Clear, technically precise answers to the most common questions about implementing the Article schema type for enhanced search visibility and entity understanding.
The Article schema type is a Schema.org vocabulary class used to markup a news, scholarly, or blog article, providing structured signals about the headline, author, date published, and publisher to help search engines understand content authorship and topical authority. It functions by embedding machine-readable metadata—typically in JSON-LD format—within the <head> or <body> of an HTML page. When a search engine crawler parses this markup, it extracts explicit entity relationships, such as the author property linking to a Person or Organization entity, and the publisher property identifying the publishing organization. This structured data enables rich result features like top stories carousels, author bylines in search snippets, and accelerated indexing for time-sensitive content. The type inherits properties from the broader CreativeWork class, allowing it to also carry signals like dateModified, image, and about to further define the content's topical focus.
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Related Terms
Mastering the Article schema requires understanding its relationship to adjacent structured data types and entity-linking properties that collectively establish content authority and authorship signals.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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