Structured Data (Schema Markup)

Definition: Structured Data is machine-readable markup (typically JSON-LD using schema.org vocabulary) that helps search engines and AI systems interpret the meaning, relationships, and attributes of on-page content.

Scope:

• Includes: JSON-LD schema; entity typing and relationship mapping; markup connecting people, organizations, products, and concepts; semantic metadata that clarifies meaning for AI and search.
• Excludes: visual or layout formatting; hidden or manipulative text intended solely to affect rankings; markup that misrepresents factual relationships.
• Notes: Structured Data improves interpretability, not style. It enables systems to categorize, cross-reference, and cite content more precisely.

Why it matters: Generative engines rely on structured meaning rather than keywords alone. Schema markup helps AI systems understand what your content is—not just what it says—enhancing both credibility and eligibility for citation in AI-generated answers.

See also: Entity (Knowledge Graph); Entity-Rich Content; Knowledge Graph; Format Discipline; Source Eligibility

References:

• Mika, Peter — schema.org “Vocabulary and Extensions”
• W3C — Semantic Web Standards
• Bizer, C., Heath, T., & Berners-Lee, T. — Linked Data — The Story So Far

Synonyms: Schema Markup; Semantic Markup; Knowledge Representation