Entities aren’t new to search. Google has been working with entity-based understanding for over a decade. What’s changed is that AI search has made entity optimization impossible to ignore. Where entities once supported traditional ranking systems behind the scenes, they now play a central role in how AI systems retrieve, understand, and cite content.
In this post, I’ll help you understand what entities actually are, why they’ve been important all along, and why the rise of AI search makes entity thinking essential rather than optional.
What Entities Actually Are (No Jargon)
An entity is a clearly defined and identifiable thing. That’s it. Not a keyword, not a phrase, but an actual concept that exists independently and can be distinguished from other concepts.
Entities include:
- Brands (Google, Salesforce, Victorious)
- People (Sundar Pichai, Rand Fishkin)
- Products (Google Analytics, iPhone 15)
- Places (San Francisco, Golden Gate Bridge)
- Topics and concepts (Core Web Vitals, technical SEO)
- Events (Google I/O, SMX Advanced)
The key difference between entities and keywords is this: Keywords are text strings people type or that appear on pages. Entities are the underlying things those text strings refer to.
When someone searches for “Delta direct flights to Paris,” Google doesn’t just match those words. It understands that “Delta” refers to Delta Air Lines (the airline), not the Greek letter, the river delta geographic formation, or Delta Faucet Company. It knows “Paris” means Paris, France (the city), not Paris, Texas or Paris Hilton. It connects these entities with the relationship “airline operates routes to destination,” then retrieves current flight information for that specific airline to that specific city.
Still unclear? Our entity glossary covers this and many more entity-related terms, plus examples and tips.
From Matching to Understanding
Traditional keyword-based search operated on text matching:
- User types words
- System finds pages containing those words
- System ranks pages based on relevance signals
- User gets a list of results
Entity-based search operates on conceptual understanding:
- User types words
- System identifies which entities those words refer to
- System retrieves content about those entities and their relationships
- System understands what information the user actually needs
- User gets results organized around entities, not just keyword matches
If you search for “who is the CEO of the company that makes Pixel phones,” a keyword system struggles because the query never mentions “Google” or “Sundar Pichai.” An entity system recognizes that “Pixel phones” refers to a product entity, that product is made by Google (company entity), and Google has a CEO relationship to Sundar Pichai (person entity). The answer comes from understanding entity relationships, not matching keywords.
This isn’t theoretical speculation about where search is headed. This is how Google has worked for years.
Entities Have Been Important All Along
The infrastructure for entity-based understanding has been building for over a decade through a series of major search updates that progressively moved away from pure keyword matching.
Knowledge Graph (2012)
Google’s Knowledge Graph is a database of entities (over 500 billion facts about 5 billion entities) and their relationships. It launched in 2012 and represented the first major public signal that search was moving toward entity understanding.
Knowledge Panels, which display structured information about entities directly in search results, are powered by the Knowledge Graph. When you search for a brand or public figure and see a box with their logo, description, and key facts, that’s entity recognition in action.
The Knowledge Graph enabled Google to start answering questions by understanding entities and their relationships rather than just matching keywords to documents.
Hummingbird (2013)
The Hummingbird update in 2013 pushed Google’s core algorithm toward semantic search and conversational queries. This was about understanding the meaning behind queries rather than just the individual words.
Hummingbird enabled Google to handle queries like “where can I find a good pizza place near me” by understanding that the user wants local business recommendations (entity type: restaurant), not pages containing those exact words.
RankBrain (2015)
RankBrain, introduced in 2015, used machine learning to interpret queries and understand how entities relate to each other. It helped Google handle queries it had never seen before by understanding conceptual relationships between entities.
If someone searched for “what’s the gray console made by Sony,” RankBrain could connect “gray console” to PlayStation even without that exact phrase appearing in the query history, because it understood entity relationships and attributes.
BERT (2019)
BERT (Bidirectional Encoder Representations from Transformers) enabled Google to better understand how words relate to each other in sentences, which is essential for proper entity recognition and linking.
BERT helps Google understand that in “2019 brazil traveler to usa need a visa,” the word “to” is critical for determining that the query is about Brazilians traveling to the US, not Americans traveling to Brazil.
The Pattern Is Clear
Marketers often treat entities as a new trend that emerged with AI search, but that’s backward. Entities have been foundational to how search works for over a decade. Where entities once supported traditional ranking systems in the background, they now drive the primary mechanism for how AI systems retrieve, understand, and cite content.
Why AI Search Makes Entities Impossible To Ignore
Traditional search retrieves pages and ranks them. You search, you get ten blue links, you click through to try to find the information you need. Entities help with retrieval and ranking, but the end goal is still to send you to pages.
Many AI search systems use Retrieval-Augmented Generation (RAG). The system:
- Retrieves relevant content from across the web
- Synthesizes information from multiple sources
- Generates a direct answer
- Cites sources (sometimes)
This process is entity-dependent at every step.
Retrieval depends on entity recognition. If AI systems can’t confidently identify which entities your content is about, your content may not be retrieved at all. Entity linking confidence, entity salience, and clear entity mentions all affect whether your page enters the candidate set.
Synthesis depends on entity relationships. AI systems need to understand how the entities in your content relate to each other and to the query. Content with clear entity relationships and well-defined entity attributes is easier to extract and synthesize.
Citation depends on entity authority. When AI systems select which sources to cite, entity authority signals matter enormously. Is your brand entity recognized as authoritative for this topic? Do you have strong entity associations with the subject matter? These factors influence attribution likelihood.
In traditional search, weak entity signals might mean you rank on page two instead of page one. AI systems don’t have a “page two.” They generate one answer. Your content is either in the evidence set or it isn’t. Entity optimization is what determines that.
What This Means for Marketers
You can’t optimize for entities the way you optimized for keywords. You can’t just “add more entities” to a page or hit some target entity density the way you might have targeted keyword density years ago.
Since entity optimization is about how AI systems understand your content and your brand, it requires a different approach.
What Entity Optimization Actually Looks Like
Clear entity identification: Make it obvious which entities your content is about. Use consistent naming, lead sections with entity names rather than pronouns, and provide context that helps AI systems link mentions to the correct real-world entities.
Instead of: “They launched a new update focused on quality.” Write: “Google launched the Helpful Content Update in August 2022 to reward people-first content.”
The second version gives AI systems clear entities (Google, Helpful Content Update) with explicit relationships (launched, temporal context) and attributes (purpose).
Entity relationship building: State relationships explicitly. Don’t imply connections or assume AI systems will infer them.
Instead of: “This tool works with popular analytics platforms.” Write: “Semrush integrates with Google Analytics, Adobe Analytics, and Google Search Console.”
Clear entity relationships are extractable and reusable in AI-generated answers.
Entity authority signals: Build recognition as an authoritative entity for specific topics through consistent, high-quality coverage over time. This demonstrate sustained expertise that gets recognized by other authoritative sources.
Entity authority comes from:
- Consistent publishing in your focus areas
- External validation (citations, mentions, co-citation with recognized authorities)
- Clear expertise markers (author credentials, case studies, original data)
- Structured signals (schema markup, verified profiles, Knowledge Panel presence)
Entity grounding: Tie your claims to recognized authoritative entities rather than vague sources.
Instead of: “Studies show page speed affects rankings.” Write: “Google’s 2020 Core Web Vitals announcement confirmed that page experience signals, including loading performance, became ranking factors in June 2021,” with a link to Google’s official announcement.
Grounding increases trust signals and extraction likelihood.
This Builds on What You Already Do
Entity optimization doesn’t replace good SEO fundamentals. It builds on them.
Good content is still good content. Technical SEO fundamentals still matter (if AI systems can’t crawl your site, they can’t retrieve your content regardless of entity signals).
What’s changed is the “why” behind many best practices. You’ve probably been told to use clear headings, consistent terminology, and authoritative sources. Those recommendations still stand, but now the reason is different: they improve entity recognition, entity linking confidence, and entity authority rather than just keyword relevance signals.
If you’ve been creating comprehensive, well-researched content with clear expertise markers, you’re already part of the way there.
Shifting to Answer Engine Optimization
Entity-based optimization redirects the focus from pages and queries to relationships and recognition. It’s about how well AI systems understand what your content is about and how strongly your brand is associated with specific topics.
Learn more about answer engine optimization and entities in our AEO strategy guide.
