EntityMap Opens Public Consultation on New Standard for AI-Readable Website Knowledge

EntityMap, a new open standard designed to help AI systems understand website knowledge more accurately, has entered a 33-day public consultation. The project gives organizations a way to publish a structured, machine-readable map of what they do, what they offer, how their key entities relate to one another, and where the supporting evidence sits on their website.

The aim is to reduce the need for AI systems to infer meaning from fragmented web pages, making it easier for search engines, retrieval systems, and large language model applications to access factual information directly from the source. The specification is available at entitymap.org/spec/v1.0. The consultation runs until 30 June 2026, with the official launch scheduled for 1 July 2026.

Developers, publishers, structured-data specialists, AI retrieval practitioners, SEO professionals, and data-quality experts are invited to review the specification, test implementation, and contribute feedback through the EntityMap community forum and GitHub repository at github.com/entitymap.

Fred Laurent, CTO of InLinks and Waikay, said: “Where a sitemap tells search engines which pages exist on a website, EntityMap tells AI systems what an organisation is, what it does and how its knowledge connects. AI systems are increasingly being asked to summarise, recommend and explain organisations. If the underlying information is fragmented, incomplete or ambiguous, machines are forced to infer relationships. EntityMap gives them a structured source of truth to work from.”

AI systems are now being used to answer questions that would historically have been asked through search engines, websites, professional advisers, or customer-service teams. Yet organizations have limited control over how those systems interpret their websites. A company’s products, services, expertise, locations, leadership, accreditations, and relationships may be spread across many pages. AI systems often retrieve small fragments of this content and reconstruct meaning probabilistically, which can lead to incomplete answers, weak attribution, or inaccurate representations.

EntityMap has been developed to address this problem by allowing organizations to publish a single structured file that declares key entities, defines relationships, and links each claim back to its source evidence. The file can be reviewed by humans before publication, then read by machines in a consistent format.

Dixon Jones, co-founder of Waikay and a long-standing specialist in search, entities, and AI visibility, said: “The web was built around pages, links and prose. AI retrieval needs a clearer layer of meaning and evidence. EntityMap is designed to help organisations say: these are the things we know, these are the relationships between them, and this is the evidence that supports those claims. This consultation is about opening the standard up to scrutiny. We want people to test it, challenge it, implement it and help improve it before the formal launch.”

EntityMap is published as a structured file at a predictable location on a website. It identifies important entities associated with an organization, such as products, services, people, topics, locations, claims, or areas of expertise. It then maps the relationships between those entities and links them to supporting pages, allowing machines to retrieve an evidence-backed view of the organization rather than relying only on isolated page fragments.

The project includes a specification, documentation, examples, and validation tools. It is published under CC BY 4.0, with no subscription, vendor lock-in, or proprietary software requirement. R.V. Guha, one of the founders of Schema.org, has reviewed the project and said: “This is a good thing for the world.”

EntityMap is relevant to any organization that needs AI systems to understand its information accurately. Potential use cases include healthcare organizations publishing accurate service, treatment, or professional information; financial services firms clarifying products, risks, advice boundaries, and regulated information; legal, professional-services, and B2B organizations with complex expertise; publishers that want clearer attribution for their knowledge and editorial content; brands concerned about how AI systems describe their products, people, or services; and technology teams building retrieval-augmented generation systems that need cleaner source data.

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