Schema.org
In one line
Schema.org is the shared vocabulary co-sponsored by Google, Microsoft, Yahoo and Yandex that lets you label what each page means so search engines and AI can understand it.
Going deeper
Schema.org is a shared structured-data vocabulary maintained jointly by Google, Microsoft, Yahoo and Yandex. It defines standard types — Product, Article, FAQPage, Organization, HowTo — so machines can recognise what a page is actually about. It exists to unify the patchwork of competing microdata standards that came before, and it has become the de facto vocabulary of the structured web. Its value only grew in the GEO era.
Mechanically the work is semantic labelling. You embed Schema.org markup as JSON-LD inside the page, and search engines plus LLMs read those labels alongside the prose. The same '4.7' that is just text in the body becomes 'aggregateRating' under schema — now it is unambiguously a rating. When models compose answers, those structured signals push both accuracy and citation likelihood up.
For GEO the schema lift is straightforward: structured facts are easier to extract. Price, rating, stock, hours, author — once they are typed, AI is far more likely to lift them straight into an answer. Useful KPIs are citation rate on schema-tagged pages and source-card frequency in AI Overviews and Perplexity. Villion correlates schema coverage with citation rate so the ROI of structured-data work is visible.
Among signals the position is clear. Body content tells the model what you say, E-E-A-T tells it who is saying it, and Schema.org tells it the shape of what you say. The three reinforce each other. AI Overviews lean on schema directly via the search index, and both ChatGPT and Perplexity treat schema markup as a parsing signal when they read pages live.
Two myths to retire. First, the assumption that schema alone earns citations. It does not — schema raises eligibility, but only pays off when the underlying copy is accurate and well organised. Second, treating schema as a one-and-done install. Pages change, schema drifts out of sync with the visible content, and that mismatch is a negative signal in itself.
Sensible next steps: prioritise schema on core pages (brand, product, FAQ, article), ship it as JSON-LD for consistency, validate with Google's Rich Results Test, audit body-versus-schema alignment quarterly, and monitor whether AI answers cite your prices, ratings and definitions correctly. Even in the GEO era, Schema.org is still the highest-leverage piece of structured-data infrastructure you can ship.
Sources
Related terms
JSON-LD
JSON-LD (JSON for Linked Data) is the JSON-based format used to embed Schema.org markup in a page — currently Google's recommended way to ship structured data.
SEOKnowledge Graph
A knowledge graph is a database of entities — people, brands, products — and their relationships, used by search engines and LLMs as the factual backbone of their answers.
GEO·AEOAI Overview
Google AI Overviews is the AI-generated summary that appears above the standard results in Google Search — one of the most prominent zero-click surfaces today.
GEO·AEOGEO
GEO (Generative Engine Optimization) is the practice of optimizing content and data so that a brand gets cited and recommended inside generative AI search answers like ChatGPT, Perplexity and Google AI Overviews.
SEOE-E-A-T
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's four-axis lens for content quality — and the same kinds of signals matter when LLMs decide whom to cite.
How does your brand show up in AI answers?
Villion measures how your brand appears across ChatGPT, Perplexity and AI Overviews, then automates the work that lifts citation rate and share of voice.
Get a free audit