Knowledge graph SEO is the practice of making the things a site is about, its organization, its people, its products, its topics, unambiguous to a search engine that stores knowledge as entities and relationships rather than as strings. The work divides into two halves: describing entities accurately in machine-readable form on the site, and corroborating those descriptions in sources the search engine already trusts. The pages below cover each part in turn.
How the pieces fit together
Google announced the Knowledge Graph in May 2012 with the phrase “things, not strings”, and the shift that phrase describes is the premise of this whole section. A search engine that holds an entity for a university, with edges to its city, its programs and its faculty, can answer questions that no amount of keyword matching would reach. Understanding that data structure comes first, in what is a knowledge graph and what is an entity.
Three layers follow from it. The description layer is structured data on the page, using the schema.org vocabulary and normally written as JSON-LD. The identity layer is the set of stable identifiers that let separate statements be recognized as being about one thing, which is the same mechanism as linked data applied to a website. The corroboration layer is everything off-site that repeats the same facts: Wikipedia, Wikidata, official profiles, licensing bodies, press coverage.
None of these is a ranking input, and no page in this section will claim otherwise. What they change is whether a search engine can resolve a mention to the right entity and describe it correctly, which is a precondition for knowledge panels, entity-aware result features, and accurate representation in AI answer surfaces.
Entity SEO
The pillar guide for the whole section. It traces the move from keyword matching to entity matching, covers the public entity catalogs a search engine reconciles against (Wikipedia, Wikidata, DBpedia and the Freebase lineage), and explains the two practitioner concepts that carry the most weight: the entity home, meaning the single authoritative URL a brand designates as the canonical description of itself, and corroboration across independent sources. Start here if the goal is a working method rather than a definition.
Read entity SEO.
What are entities in SEO?
A short definitional page for the specific question. It sets out what counts as an entity in a search context, why a search engine treats “Marie Curie”, “Curie” and “the discoverer of polonium” as references to one node rather than three phrases, and how entities differ from keywords in practice. It is the fastest way to get the vocabulary straight before reading anything more tactical.
Read what are entities in SEO.
Semantic SEO
Semantic SEO is content strategy organized around meaning rather than around query strings: covering a topic completely, naming the entities and relationships a topic involves, and structuring a page so that its subject is explicit. This page covers query interpretation, the difference between semantically related terms and synonym stuffing, and how to plan a page around what a topic actually contains.
Read semantic SEO.
Topical authority
Topical authority is what accumulates when a site covers a subject area consistently and connects that coverage internally. This page treats a site’s own pages and links as a small graph, examines how entity coverage and internal linking make a subject area legible, and is honest about what part of the concept is documented by search engines and what part is practitioner model. It also covers topical maps as a planning artifact.
Read topical authority.
sameAs schema
The sameAs property states that the entity described in a page’s markup is the same thing as an entity identified elsewhere. It is the single most direct way to connect a site’s own statements to identifiers a search engine already holds, most usefully a Wikidata Q-ID. This page gives the exact JSON-LD, explains what belongs in the array and what does not, and covers the failure mode of pointing sameAs at a related thing rather than an identical one.
Read sameAs schema.
Organization schema markup
A complete, copy-paste Organization block with every property worth including, and a working explanation of each. It covers the properties Google documents for its organization features, such as logo and contact details, alongside the properties that exist purely to describe the entity, such as founder, foundingDate and parentOrganization. It also covers where the block should live and why one @id reused site-wide beats a copy on every page.
Read organization schema.
Person schema markup
The counterpart for people: authors, executives, practitioners and personal brands. The page gives a full Person block, covers worksFor, jobTitle, knowsAbout and alumniOf, and explains how author markup relates to expertise signals and to knowledge panels for individuals. Person entities are also where sameAs earns the most, because professional identifiers such as ORCID and official profiles are unusually reliable targets.
Read person schema.
Structured data testing tools
Markup that has not been validated is a guess. This page walks through Google’s Rich Results Test, the schema.org Schema Markup Validator, the JSON-LD Playground and Search Console’s enhancement reports, explains what each one can and cannot tell you, and covers generators along with their limitations. The important point is that no single tool answers every question, since Google’s tools report on Google’s supported features and schema.org’s tool reports on the full vocabulary.
Read structured data testing tools.
JSON-LD schema examples
Eight copy-paste JSON-LD templates for the entity markup jobs SEOs do most: an entity home, an author, an article that references both by @id, nested organizations, a college or university, Wikidata links and FAQ markup. Each one parses as written and says what to change and what to leave alone. The last template joins the others into one site-wide graph.
Read JSON-LD schema examples.
Entity SEO tools compared
A comparison of entity SEO tools by the job each one does: generating markup at scale, extracting entities, entity-driven internal linking, knowledge panel work and content optimization. Every capability comes from the vendor’s own documentation, and nothing is ranked. It closes with what no tool can do.
Read entity SEO tools compared.
Knowledge graphs, AI search and LLMs
The section’s point-of-view piece on generative engine optimization. It separates what is documented, how retrieval-augmented generation works and how AI answer surfaces cite sources, from what is practitioner inference about entity clarity affecting how a brand is represented. It makes no promises about visibility in model outputs, and it connects to the developer-side counterpart, retrieval over a graph.
Read knowledge graphs, AI search and LLMs.
Where to start
For a site with no structured data, the order that produces results fastest is: an Organization block with a stable @id and a sameAs array, Person blocks for the people who publish, validation of both, then the documented rich result types for the templates that matter commercially. Off-site corroboration and Wikidata work come after the site’s own statements are consistent, because a search engine reconciling a brand against external sources first needs a coherent thing to reconcile.
For a site that already has markup, the first job is an audit for contradiction rather than an expansion of coverage. Two plugins each publishing an organization node, or an author name that differs between the byline and the markup, does more damage than a missing property.
FAQ
What is knowledge graph SEO?
Knowledge graph SEO is the practice of describing a site’s entities in machine-readable form and corroborating those descriptions in sources a search engine already trusts, so the engine can resolve mentions of a brand, person or product to the correct entity. It centres on schema.org markup, stable identifiers, and consistency between on-site and off-site facts.
Does schema markup improve rankings?
No, not directly. Google documents structured data as something that makes pages eligible for rich results and helps search engines understand content, not as a ranking factor. What it can change is how a result is presented and whether an entity is identified correctly, both of which affect outcomes without being a scoring input.
How does a brand get into the Google Knowledge Graph?
There is no submission form. Google builds its graph from many sources, including Wikipedia, Wikidata, licensed data and the open web, so the practical route is to publish a clear entity description on the site, link it with sameAs to identifiers that already exist, and ensure independent sources state the same facts consistently.
Is entity SEO different from semantic SEO?
They overlap and answer different questions. Entity SEO is about identity: making sure a search engine knows which thing a page is about and can connect it to what it already knows. Semantic SEO is about coverage and meaning: organizing content around topics and the relationships within them rather than around query strings.
Sources and further reading
- Google, “Introducing the Knowledge Graph: things, not strings” (2012): https://blog.google/products/search/introducing-knowledge-graph-things-not/
- Google Search Central, Introduction to structured data markup: https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data
- Google Search Central, structured data general guidelines: https://developers.google.com/search/docs/appearance/structured-data/sd-policies
- Google Search Central documentation, knowledge panels: https://support.google.com/knowledgepanel/answer/9163198
- schema.org home: https://schema.org/
- Wikidata, Introduction: https://www.wikidata.org/wiki/Wikidata:Introduction