Entity SEO

Entity SEO is the practice of making the real-world things behind your site, such as your organisation, founders, products, locations and core topics, clearly recognisable to search engines and AI systems. Where keyword SEO asks which strings people type, entity SEO asks whether machines know who you are, what you offer and how you relate to other entities.

How it works

Search engines store entities and their relationships in a knowledge graph. Google described this shift in 2012 as moving from “strings” to “things”. An entity is easier to recognise when the same facts appear consistently in several places: your own content, structured data, and external sources such as Wikidata, LinkedIn or Crunchbase. LLMs rely on similar signals when deciding whether they can describe and cite a brand with confidence.

MLforSEO’s entity work breaks this into steps: inventory your entities, prioritise them, audit how they are extracted from your pages, fix gaps, connect them with schema, keep them consistent across platforms, and review them on a schedule.

How it’s used in SEO and marketing

• Running an entity audit with an NLP API to check that your brand is extracted, correctly typed and salient on key pages.
• Implementing Organisation, Person and Product schema with @id and sameAs so markup forms one connected graph.
• Fixing disambiguation problems when your brand shares a name with something else.
• Building entity hub pages instead of thin keyword landing pages.
• Improving visibility and citations in AI search, where systems select sources they can identify confidently.

Example

An entity audit runs the Google Cloud Natural Language API over your top pages. If your brand is missing from the top extracted entities, has salience well below 0.5, or has no Knowledge Graph ID, the fix starts with the H1 and opening paragraph, consistent naming, and Organisation schema that points to verified external profiles.

Limitations and things to watch

• Entity work compounds slowly. Schema alone rarely moves results; consistency across sources matters more.
• Trying to optimise every entity at once is a common failure. Prioritise a small core set first.
• Entity data decays. Rebrands, new products and staff changes need a maintenance cadence.
• Tools differ: an entity one NLP API finds, another may miss, so treat outputs as signals rather than verdicts.

Learn it on MLforSEO

• AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands course
• The AI-powered Entity Audit course, the full audit workflow.
• How to Audit Entity Structure in Modern Search
• How Entity Blueprints Turn Brand Knowledge into Search Signals
• The Three-Tier Entity Prioritisation Framework