Entity SEO. Beyond keywords. Built for Google and the AI engines that came after it.

What an entity is

An entity, in SEO and information-retrieval terms, is a distinct identifiable thing in the world. Google's own definition: "a person, place, or thing that is singular, unique, well-defined and distinguishable". The city of Perth is an entity. So is the trade of electrician. So is the concept of emergency response. So is the business Brightspark Electrical. So is the suburb of Joondalup.

The shift from keywords to entities is one of the most important changes in search of the last decade. Google started moving in this direction with the Hummingbird update in 2013 and accelerated through RankBrain, BERT and MUM. By 2024, every modern Google ranking system thinks in entities first and keywords second. The keyword is now a clue about which entities the searcher meant. The entities are what the system is actually scoring against.

A simple way to test this: search "Perth weather" in Google. The top result is a Knowledge Graph card for Perth (the entity) showing live weather data, no traditional ranking page involved. Google understood the query as "weather forecast for the entity 'Perth, Western Australia'" and answered from its entity database directly.

For your pages to win in this environment, they need to be parsed cleanly by Google's entity systems. Which entities is the page about? Which entities does it connect to? Which known entities (the dentist, the suburb, the regulator) is it explicitly tied to? Entity SEO is the practice of making those answers obvious.

The Knowledge Graph explained

The Knowledge Graph is Google's database of entities and the relationships between them. It currently contains hundreds of billions of facts about billions of entities. Every entity in the Graph has an ID (a unique identifier), a set of types (person, place, organisation, etc.), and a set of connections to other entities.

Example: the entity "Perth, Western Australia" in the Knowledge Graph has:

• Types: City, AdministrativeArea, TouristDestination.
• Properties: population, area, mayor, time zone, climate type.
• Connections: located in Western Australia, located in Australia, contains the suburb Fremantle, contains the suburb Joondalup, served by Perth Airport, has dialling code 08.

When Google reads your page, it tries to map the text against the Graph. If your page talks about Perth, Google asks: which Perth? The one in Western Australia? Perth, Scotland? Perth, Ontario? The page that disambiguates clearly (by mentioning Western Australia, Joondalup, Fremantle, the Swan River) gets matched correctly. The page that just says "Perth" three times leaves Google guessing.

Entity SEO is about giving Google enough disambiguating signal that there is no guessing. Mention the connected entities. Use schema markup that names the specific entity ID where possible. Link to authoritative sources (Wikipedia, official sites) where appropriate. Make the entity unambiguous.

Why entity SEO matters in 2026

Three reasons it has become non-optional.

1. Google's ranking systems work natively in entity space. Recent leaks and confirmations (the Content Warehouse documentation in 2024) show Google's internal scoring uses entity-based features alongside the older lexical features. Pages that cover the expected entities for a query score better.

2. AI Overviews and SGE pull from entity-rich pages first. When Google generates an AI Overview, it preferentially summarises pages that have clean entity signals (proper schema, clear topical structure, named connections). Sites that ignore entity SEO get summarised less often.

3. AI engines outside Google use the same approach. ChatGPT Search, Perplexity, Claude with web search, and Gemini all extract entities from the pages they read. Entity-rich pages get cited more. Entity-thin pages get treated as background context. See the AI Search pillar for the broader picture.

The shift is uneven across queries. For long-tail "near me" searches the entity signal is everything (which suburb? which trade? which business?). For broad informational queries entity coverage is what differentiates the page that wins the snippet from the page at position six. Either way, the trend line points one direction.

The five-step entity SEO framework

This is the process we run on every cluster we write. Steal it.

1. Step 1: Identify the primary entity. What is the one thing this page is about? "Emergency electrician services in Perth" is two entities: the trade (electrician), the place (Perth). The page should make both unambiguous in the first 200 words.
2. Step 2: Identify the supporting entities. What other entities does the topic require? For emergency electrician Perth: the suburbs you serve (each a named entity), the trade body (EnergySafety WA), the specific work (powerpoint repair, switchboard upgrade, RCD installation), the typical customer (homeowner, business owner).
3. Step 3: Cover each entity explicitly in the body copy. Mention every supporting entity at least once. Connect them: "We service Joondalup, Cockburn, Fremantle and the wider Perth metro area" names four entities and connects them all.
4. Step 4: Add schema markup for the entities Google cares about. LocalBusiness for the business, Service for the offering, Place for the area served, Person for the named tradesperson. JSON-LD in the head.
5. Step 5: Link to authoritative sources for ambiguous entities. A page about "asbestos roofing in Perth" should link to the WA Department of Health's asbestos guidance. The link is a citation that tells Google your page is connected to recognised authorities.

Done well, the page reads like normal prose to a human and like a clear entity map to Google. No keyword stuffing, no awkward repetition. Just names and connections.

Schema markup essentials

Schema markup is the formal way to declare entities to Google. It is JSON-LD added to the head of your page:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "LocalBusiness",
  "name": "Brightspark Electrical Perth",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Example Road",
    "addressLocality": "Joondalup",
    "addressRegion": "WA",
    "postalCode": "6027",
    "addressCountry": "AU"
  },
  "telephone": "+61 8 9000 0000",
  "url": "https://example.com.au/"
}
</script>

That schema tells Google: this page is about a LocalBusiness entity called Brightspark Electrical Perth, located at this address, reachable on this phone number. Google can now match the entity against its Knowledge Graph and treat the page as a definitive source on that business.

The schema types every business should use

• Organization (or LocalBusiness for local trades) on the homepage. Verified address, phone, ABN where appropriate.
• Person on author bio pages. Real photo, role, employer.
• Article on every blog post and cluster. With author, publish date, last reviewed date.
• BreadcrumbList on every page deeper than the homepage.
• FAQPage on pages with structured Q&A. Wins richer SERP results.
• Service on service pages. With area served and price range.
• Product on e-commerce product pages.
• Review or AggregateRating where you have genuine customer reviews.

Schema validates in Google's Rich Results Test. Anything not validating is invisible to Google. Run every page through the tester at least once.

The bridge to AI search

Entity SEO is the closest thing on-page SEO has to a future-proofing strategy. Here is why.

AI engines do not return ranked lists. They return summarised answers, sometimes with citations. To produce that summary, they read a set of pages, extract entities and the claims about those entities, then synthesise an answer that mentions the strongest, most-cited entities. The pages that get cited in the answer are the pages that made their entities clear and connected.

Three things AI engines disproportionately reward:

1. Clean schema. The structured data tells the engine exactly what entities the page covers. No interpretation required.
2. Named connections. "Perth Hills Tourism Centre" is one entity. "Perth Hills" is another. "Tourism Centre" is a third. AI engines love pages that name the connections explicitly.
3. Authoritative outbound links. Linking from your page to recognised authorities anchors your entity in the broader graph. AI engines treat outbound citations as a strong trust signal.

None of this requires changing how you write. It requires being precise about what you write about. The same on-page work that helps Google's traditional ranking also feeds the AI summary layer. Two SERPs, one investment. See the AI Search pillar for the deep version of this conversation.

Mistakes to avoid

Tools and checklists

1. Google's Knowledge Graph Search API. Type your business name. If it returns a kgmid (Knowledge Graph entity ID), Google already recognises you as an entity. Cite that ID in your schema with the sameAs property.
2. Rich Results Test. Free. Validates your schema and shows what Google extracts as structured data.
3. Schema Markup Validator. Free at validator.schema.org. Cleaner than Rich Results Test for non-Google schema types.
4. InLinks. Paid tool that surfaces entity gaps against top-ranking pages. Useful for finding entities your competitors covered that you missed.
5. Wikipedia. Surprisingly useful. If your entity has a Wikipedia page, the Wikipedia infobox is essentially the entity's Knowledge Graph profile. Link to it from your page for canonical disambiguation.
6. Our free SEO audit tool. Flags missing schema and likely entity-coverage gaps.

A pre-publish entity checklist

1. What is the primary entity of this page?
2. Have I named every relevant supporting entity in the body?
3. Have I disambiguated every place entity?
4. Have I added the right schema type(s) in JSON-LD?
5. Does the schema validate in Rich Results Test?
6. Have I linked to recognised authorities for any sensitive entity?
7. Is the sameAs property used to link my brand entity to LinkedIn, Facebook, Google Business Profile?
8. Have I avoided cramming irrelevant entities just for "coverage"?

Perth and WA context

Trades. A typical Perth tradie page mentions "Perth" three times and nothing else. We rewrite to mention the suburbs served by name (Joondalup, Cockburn, Fremantle, Mandurah), the trade body (EnergySafety WA, Plumbers Licensing Board), the relevant compliance (RCD requirements under AS/NZS 3000), and the typical local terms (Western Power for the electricity network, Water Corporation for water). Each entity is a node in the graph. The page now has 20 connections instead of three. See SEO Joondalup, SEO Fremantle and trades SEO.

Mining services. Industrial pages benefit enormously from naming the specific commodity (iron ore, lithium, nickel, gold), the specific site or region (Pilbara, Goldfields, Mid West), the specific equipment (Caterpillar 793F, Komatsu 930E), and the relevant regulator (DMIRS). Generic mining services pages get summarised away by AI engines. Entity-rich ones get cited. See mining SEO and SEO Karratha.

Healthcare. A Perth medical practice should name the practitioner, the AHPRA registration, the practice location, the specialisation, the relevant condition entities (e.g. "knee osteoarthritis", "rotator cuff repair"), and recognised guidelines (RACGP, Australian Commission on Safety and Quality in Health Care). Each anchor point adds entity credibility. See healthcare SEO and the related E-E-A-T chapter.

Legal. Family law content should name the relevant act (Family Law Act 1975), the relevant court (Federal Circuit and Family Court of Australia), the relevant body (Law Society of WA), and the specific concept entities (parenting orders, property settlement). Generic legal content with none of these gets crowded out by both Google and the AI engines. See legal SEO.

Local SEO interactions. Entity SEO and local SEO intersect heavily. A well-structured LocalBusiness schema with the correct entity properties helps Google place you in the local pack for the right suburb. See Local SEO Perth for the service side.

For the parent on-page picture, return to On-Page SEO. For the schema-and-AI dimension at greater depth, the AI Search pillar is the natural next read. And for the related layer of demonstrating real expertise, E-E-A-T explained sits next to this chapter.