An entity graph is a machine-readable set of interconnected schema.org nodes (Organization, Person, WebSite, Article) tied together by stable @id references and linked outward via sameAs.
An entity graph is a machine-readable set of interconnected schema.org nodes (Organization, Person, WebSite, Article) tied together by stable @id references and linked outward via sameAs.
Search engines and answer engines use entity graphs to disambiguate names and attach content to the right entity. Wikidata is a common external anchor.
Build an entity graph for your business by publishing a schema.org Organization node with a stable @id and a sameAs array linking to authoritative external profiles — Wikipedia, Wikidata, LinkedIn, Crunchbase, official social accounts — then referencing that same @id from every Article, QAPage, and Person node on the site.
Author entities matter for AI citations because grounded answer engines need to name a source's author with confidence — and a schema.org Person node with a stable @id, real credentials, and sameAs links to authoritative profiles is the cleanest signal that a page has a named human behind it.
A knowledge graph is a database of real-world entities — people, places, organizations, works — and the relationships between them, used by search engines and answer engines to disambiguate names and attach content to the right entity instead of a matching string.