A shared vocabulary for labelling web content so that search engines and AI systems understand what a page is about — whether it is a product, an article, a business, or a person.
A shared vocabulary for labelling web content so that search engines and AI systems understand what a page is about — whether it is a product, an article, a business, or a person.
Schema.org was launched in 2011 by Google, Microsoft, Yahoo!, and Yandex to provide a common shared vocabulary for structured web data. Its vocabulary covers hundreds of entity types and thousands of properties, enabling publishers to annotate product prices, review ratings, event dates, organisational details, and much more in a standardised, machine-readable format. Schema.org markup is embedded in HTML using JSON-LD (preferred), Microdata, or RDFa. Rich results in Google Search (star ratings, price ranges, FAQ panels) depend on Schema.org. For AI systems, Schema.org markup is a primary signal for entity identification, attribute extraction, and trust assessment — products with complete, accurate Schema.org Product markup are significantly more likely to be correctly identified and recommended by AI shopping systems.
E-commerce teams should prioritise Schema.org Product markup completeness as a direct lever for AI shopping visibility — ensuring all key attributes (name, description, price, availability, reviews, brand, GTIN) are marked up accurately.
Like a standardised product barcode system — every item carries a label in a format that all scanners (search engines, AI systems) can read, regardless of manufacturer.
A shared vocabulary for labelling web content so that search engines and AI systems understand what a page is about — whether it is a product, an article, a business, or a person.
Schema.org was launched in 2011 by Google, Microsoft, Yahoo!, and Yandex to provide a common shared vocabulary for structured web data. Its vocabulary covers hundreds of entity types and thousands of properties, enabling publishers to annotate product prices, review ratings, event dates, organisational details, and much more in a standardised, machine-readable format. Schema.org markup is embedded in HTML using JSON-LD (preferred), Microdata, or RDFa. Rich results in Google Search (star ratings, price ranges, FAQ panels) depend on Schema.org. For AI systems, Schema.org markup is a primary signal for entity identification, attribute extraction, and trust assessment — products with complete, accurate Schema.org Product markup are significantly more likely to be correctly identified and recommended by AI shopping systems.
E-commerce teams should prioritise Schema.org Product markup completeness as a direct lever for AI shopping visibility — ensuring all key attributes (name, description, price, availability, reviews, brand, GTIN) are marked up accurately.