Schema Markup for AI
Schema markup (structured data) helps AI systems understand your content type, entities, and relationships. Using JSON-LD format, schema markup provides explicit context that AI systems use to better comprehend and cite your content.
Definition
Schema markup, also known as structured data, uses Schema.org vocabulary to provide explicit information about your content to search engines and AI systems. For AI citations, schema markup helps AI systems understand what your content is about, who created it, when it was published, and what entities it mentions. Common schema types for AI optimization include Article (for blog posts), Person (for authors), Organization (for companies), FAQPage (for questions and answers), HowTo (for tutorials), and BreadcrumbList (for navigation). AI systems parse schema markup to extract structured information, understand content context, and make better citation decisions.
Examples
Article schema with author, datePublished, and headline helps AI systems identify authoritative, recent content.
FAQPage schema with question-answer pairs makes your content more likely to be cited for direct questions.
Person schema with jobTitle and affiliation helps AI systems understand author expertise and credibility.
How to Apply
- 1.Use JSON-LD format (preferred by AI systems) for schema markup
- 2.Implement Article schema for blog posts with author, datePublished, and headline
- 3.Add Person schema for authors with credentials and expertise
- 4.Include FAQPage schema for question-answer content
- 5.Use Organization schema for company information
- 6.Validate schema markup using Google Rich Results Test
Related Tools
Related Tools
Complement your analysis with these AI citation optimization tools: