--- title: "Define your brand, authors, and topics as entities Google can recognize" description: "Entity SEO in practice: how the Knowledge Graph works, how to mark up organizations, people, and products, and how to build topic clusters around entities." canonical: "https://scalewithsearch.com/articles/entity-seo-knowledge-graph" date: "2026-03-20" modified: "2026-09-25" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - 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Google stopped matching keywords alone a long time ago. For the query "best pizza NYC," it recognizes pizza as a food, NYC as a place, and "best" as a sign that the searcher wants reviews. It reads the query as things and their relationships. Those things are entities. Entity SEO makes your site's entities clear: your organization, your authors, your products, and the topics you cover. It also connects them to the entities Google already knows in its Knowledge Graph. Keyword research still matters. Keywords show what people type. Entities describe what the page is about. This article explains entities, the Knowledge Graph, the difference from keyword work, and the practical steps: topic clusters, structured data, author profiles, and measurement. ## What an entity is An entity is a thing or concept that exists on its own and can be identified without ambiguity. It has attributes and relationships. | Type | Example | Attributes | |---|---|---| | Person | Albert Einstein | Physicist, born 1879, Nobel Prize in Physics 1921 | | Place | San Francisco | City in California, coordinates | | Organization | Google | Founded 1998, headquarters in Mountain View | | Product | iPhone 15 | Smartphone, made by Apple, released 2023 | | Concept | Theory of relativity | Physics theory, developed by Einstein | Take the query "Who developed the theory of relativity?" A keyword approach looks for documents that contain "developed," "theory," and "relativity." An entity approach reads "who" as a request for a person, "developed" as a creator relationship, and "theory of relativity" as a known concept. The answer is the person entity with that relationship: Albert Einstein. ## How the Knowledge Graph works Google's Knowledge Graph is a database of entities and the facts that connect them. It feeds knowledge panels, the fact boxes that appear for well-known people, places, and organizations. It also informs entity carousels and related searches. Featured snippets are a different system. Google extracts them from web pages, not from the Knowledge Graph. Do not confuse the two when you set goals. Google builds the Knowledge Graph from many sources: - Wikipedia and Wikidata; - licensed data and public data sets; - structured data on web pages; - mentions of entities across the web; - Freebase, the open database Google closed in 2016, whose data moved into Wikidata and the Knowledge Graph. The graph also resolves ambiguity. "Apple" can mean the company or the fruit. Google uses the query, the context, and the searcher's location to pick one. Your page has to supply the same context. A page that mentions "Apple" next to "iPhone" and "Cupertino" leaves no doubt. Structured data can remove the rest. ## How entity work differs from keyword work | Keyword approach | Entity approach | |---|---| | Match the query terms | Match the intent behind the query | | Repeat the phrase | Cover the entity's attributes and relationships | | Optimize one page | Build a cluster of connected pages | | Collect links | Earn links and show expertise in the topic | | Stand-alone articles | Linked pages around one topic | A keyword approach to "best running shoes 2026" is one post that repeats the phrase in every heading. An entity approach builds a cluster: - a pillar page, "Running shoes guide," that covers types, materials, and brands; - cluster pages on trail shoes, marathon shoes, and specific brands; - links between them that connect running shoes to marathons, trail running, and the brands; - Product, Brand, and review markup where the pages describe real products. The cluster covers the entity. One page covers one phrase. ## Build topical coverage around each core entity Topical authority means a site covers a subject well enough that searchers and Google treat it as a reliable source on that subject. You build it with coverage and connection. Cover the entity's parts. A coffee site covers brewing methods (French press, espresso, pour-over), bean species (Arabica, Robusta), origins (Ethiopia, Colombia, Brazil), and equipment (grinders, machines, filters). Link the pages to each other and to the pillar. Cite primary sources. Publish steadily in the topic. The [topical authority strategy guide](/articles/topical-authority-content-strategy) covers cluster planning in depth. ## Find the entities your site depends on List the entities at the center of your business and your content. Four sources help: - Google Autocomplete, which shows how people combine your topic with other entities; - People Also Ask, which shows related questions; - Wikipedia, where most established entities have a page with attributes; - Wikidata, which stores entities with structured properties and stable identifiers. For a fitness site, the core entities are exercise, nutrition, weight loss, and strength training. Related entities include protein, cardio, muscle, calories, and body mass index. Then build the pillar and clusters. A pillar such as "Strength training guide" links to each cluster page. Cluster pages cover "Strength training exercises for beginners," "How to build muscle," and "Strength training for women." Each cluster page links back to the pillar. ## Describe entities with structured data Schema.org markup states what an entity is and what its attributes are. Without it, Google infers the entity from context. With it, you state the entity directly. Google says structured data does not raise rankings by itself. It makes a page eligible for rich results and removes doubt about what the page describes. The common types: - `Article` for posts and news; - `Product` for items you sell; - `LocalBusiness` for shops and service businesses; - `Person` for authors; - `Organization` for companies. `FAQPage` and `HowTo` markup exist too. Since August 2023, Google shows FAQ rich results only for authoritative government and health sites. It stopped showing HowTo rich results in September 2023. Use those types only when the markup has another purpose. An `Article` example: ```json { "@context": "https://schema.org", "@type": "Article", "headline": "How to Build Muscle Mass", "author": { "@type": "Person", "name": "Jordan Example", "jobTitle": "Certified Personal Trainer", "url": "https://example.com/authors/jordan-example" }, "datePublished": "2026-02-08", "publisher": { "@type": "Organization", "name": "Example Fitness", "logo": { "@type": "ImageObject", "url": "https://example.com/logo.png" } } } ``` This markup defines three entities: the article, the author, and the publisher. Check it in Google's Rich Results Test and the Schema.org validator. Mark up only what the page contains. Product markup on a blog post that reviews ten products does not describe a product page. The [schema markup types guide](/articles/schema-markup-types-guide) lists which type fits which page. ## Write with related entities, not repeated phrases Search systems learn relationships from how entities appear together across the web. A page that names the related entities shows that the author knows the subject. Weak: > Strength training is great for muscle. Strength training helps you build muscle. Strength training is the best way to gain muscle. Strong: > Strength training, also called resistance training, causes muscle hypertrophy through progressive overload. Core lifts include the squat, the deadlift, and the bench press. [Cite a named study, with journal and year, for any claim about training frequency.] The strong version names related entities: resistance training, hypertrophy, progressive overload, and specific lifts. It introduces the second term once, as an equal name, and does not rotate it for variety. Every factual claim gets a named source. Use entity names as anchor text in internal links. "Choosing the right running shoes matters for marathon training" tells the reader and Google what the linked page is about. "Learn more here" does not. The [semantic SEO guide](/articles/semantic-seo-nlp) covers how language models read these relationships. ## Make your authors and organization recognizable Google connects authors to topics when the author appears consistently across the web. For each author: - publish a bio page with credentials, degrees, and certifications; - link the bio to the author's LinkedIn and X profiles; - publish under the same name on other reputable sites; - mark up the author with `Person` and list the profiles in `sameAs`. ```json { "@context": "https://schema.org", "@type": "Person", "name": "Jordan Example", "jobTitle": "Certified Personal Trainer", "url": "https://example.com/authors/jordan-example", "sameAs": [ "https://www.linkedin.com/in/[handle]", "https://x.com/[handle]" ] } ``` Do the same for the organization. Put `Organization` markup on the home page or the about page. List the Wikipedia entry if one exists, the Wikidata item, and the official social profiles in `sameAs`. A local business also claims its Google Business Profile. Knowledge panels appear for entities with enough independent coverage. Notability usually comes from independent coverage: press, books, awards, and a Wikipedia article that meets Wikipedia's notability guideline. Wikipedia's conflict-of-interest guideline discourages writing an article about yourself or your company. When a knowledge panel exists, the person or organization it describes can claim it through Google's verification process and suggest corrections. The [E-E-A-T guide for writers](/articles/eeat-content-writers) covers the author side in more detail. Earn links from sites that are authorities in the same topic. A link from a respected medical publisher to a diabetes guide carries more topical weight than a link from an unrelated directory. Original research, guest articles on niche sites, and expert roundups are the usual routes. ## Check salience with Google's Natural Language API Salience measures how central an entity is to a text. Google Cloud's Natural Language API returns a salience score between 0 and 1 for each entity it finds. ```json { "name": "strength training", "type": "OTHER", "salience": 0.68 } ``` Run a draft through the API and read the top entities. If the entity the page targets is not near the top, the page is about something else. Rewrite the introduction and headings so the target entity is the subject. Do not add repetitions to raise the number. The API is a Google Cloud product, not the Search ranking system, so treat the score as a writing check. ## Measure entity coverage Track four groups of signals: - **Coverage.** The number of pages per core entity, and the internal links between them. - **Markup health.** The share of key pages with valid structured data, and zero errors in Search Console's enhancement reports. - **Topical reach.** The number of distinct queries per entity that bring impressions in Search Console, and featured snippets for those queries. - **Recognition.** A knowledge panel for the brand or the lead author, the brand in Autocomplete, and related questions in People Also Ask. For queries, filter Search Console by the entity name. Track the variants, for example "what is strength training," "strength training benefits," "strength training vs cardio," and "strength training for beginners." Rankings for many variants of one entity show that the cluster works. Tools for each job: - entity extraction: Google Cloud Natural Language API and spaCy; - markup checks: the Rich Results Test and the Schema.org validator; - research: Wikipedia, Wikidata, and the Knowledge Graph Search API; - cluster plans: Ahrefs Content Explorer, AnswerThePublic, and AlsoAsked. ## Avoid these mistakes **Keyword density instead of coverage.** Fifty repetitions of "strength training" build nothing. Coverage of hypertrophy, progressive overload, and program design builds the entity. **Markup that does not match the page.** A listicle marked up as one Product confuses the entity. Use markup that describes the page as it is. **Missing relationships.** An article about the iPhone that never names Apple leaves out context. Name the brand for a product and the affiliations for a person. **Orphan articles.** Ten articles on one topic without links between them read as ten separate pages. Link them into a cluster. **Anonymous bylines.** A page without a named author cannot build author recognition. Name the author, and link a bio with credentials. Aim each page at one primary entity and a few supporting entities. A page that targets many entities has no clear subject. 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