Schema.org / JSON-LD

Definition

Schema.org markup refers to the standardized annotation of structured data on websites to make their content machine-readable and semantically understandable for search engines and other applications. Schema.org was launched in 2011 by Google, Microsoft, Yahoo, and Yandex to provide a shared vocabulary for describing entities, properties, and relationships.

Schema.org includes a collection of types and properties used to enrich HTML code with semantic meaning. The goal is for search engines to interpret context more precisely and display content more attractively in search results—for example, as rich snippets or rich results.

Examples of Applications

  • Products with price, availability, and rating
  • Events with date, location, and ticket information
  • Recipes with ingredient lists, preparation time, and nutritional information
  • Organizations with contact details, logos, and opening hours
  • Local businesses with address, phone number, and geocoordinates

Target Groups

  • Website operators and SEO specialists
  • Developers of web applications and CMS platforms
  • E-commerce companies, event organizers, and publishers
  • Institutions with extensive structured information (e.g., libraries, museums)

Benefits

  • Improved visibility: Content appears with enhanced presentations (rich results) in search engines
  • Higher click-through rate (CTR): Attractive and informative snippets encourage clicks
  • Better machine understanding: Enables more precise indexing and contextual classification
  • Interoperability: Unified vocabulary simplifies data exchange and reuse
  • Future-proofing: Compatible with semantic web technologies

Key Components

  • @context: Specifies the vocabulary used (usually "https://schema.org")
  • @type: Defines the type of the described entity
  • Properties: Relevant attributes according to the Schema.org vocabulary
  • Implementation formats: JSON-LD (recommended), Microdata, RDFa

Priorities

  • Correct and complete implementation of relevant properties
  • Faithfulness to visible page content (no “hidden” data)
  • Use of the latest Schema.org version
  • Regular testing with tools such as Google’s Rich Results Test
  • Adapting to relevant search result formats

Trends

  • Expanded schema types for new industries (e.g., healthcare, education)
  • Combining structured data with AI-powered content analysis
  • Growing use for voice search and voice assistants
  • Integration into CMS and e-commerce platforms as a standard feature