Entity SEO is optimizing web content for entities rather than just individual keywords. By understanding and catering to entities, search engines can better comprehend your content’s meaning, context, and relationships to provide more relevant results aligned with user intent. My name is Casey, and I have been using Entity SEO since 20012. Just this year, I began teaching Search Engine Optimization (SEO), which includes focusing on entity usage.
The rise of entity SEO acknowledges that modern search has moved beyond simply matching words to web pages. With advancements like Google’s Knowledge Graph and natural language processing, search engines can now identify and map connections between definable entities like people, places, things, concepts, and ideas.
Focusing only on keywords limits relevance potential. An entity-first strategy builds a rich knowledge base, allowing Google to associate your content with the entities users search for. This strategy future-proofs SEO by adapting to rapidly evolving search technology.
TL;DR
- Entity SEO goes beyond keywords, using entities to help search engines understand your content and user intent.
- Entities connect concepts and provide search context, mimicking how humans understand the world.
- The Knowledge Graph and advancements in understanding relationships show Google’s focus on entities.
- Optimize for entities by researching, using internal linking and schema markup, and creating authoritative content.
- The future of SEO prioritizes entities and natural language processing, making semantic comprehension key.
Understanding Entity SEO
What is an entity?
According to Wikipedia, “An entity is something that exists as itself. It does not need to be of material existence.” It doesn’t have to be a physical, tangible object—entities can represent abstract ideas, events, or other concepts.
Tangible examples could include specific products (“iPhone 14”), businesses (“Apple Inc.”), locations (“Eiffel Tower”), or people. Abstract entities may be broader topics like “mobile photography,” philosophical concepts like “existentialism,” or colors.
The key characteristic is that an entity must be definable and distinguishable as a unique “thing” that exists. This allows search engines to identify entities within content and queries accurately.
Why are entities important?
Entities are critical because they enable search engines to understand the meaning and context behind searches rather than just matching fragmented keywords and strings of text. They can grasp that “iPhone 14” refers to Apple’s latest smartphone model, establishing the relationships between the product entity, company entity, category entity, etc.
By comprehending how different entities relate to one another in various contexts, search engines can serve up content precisely aligned with the user’s overarching informational needs behind the query. Results move from mere keyword matches to relevant, semantically mapped concepts.
Entities effectively make search engines smarter and more analogous to how humans understand the real world. We don’t think of disconnected keywords but coherent concepts of multiple entities and their relationships.
Google’s history with entities
Google unveiled its Knowledge Graph in 2012, representing a major philosophical shift from the dated keyword-matching paradigm to an entity-based model leveraging structured data. The Knowledge Graph ingested information from sources like Wikipedia and Wikidata to build a massive knowledge base containing billions of entities and their properties/relationships.
The rollout of Knowledge Panels soon followed, providing rich entity information pulled from the Knowledge Graph directly in search results. This marked Google’s first tangible steps towards truly understanding entities, not just keywords.
Over the past decade, Google has continually refined its ability to identify entities, discern context, and map how different entities are associated through neural machine learning and natural language processing techniques. Overall, Google’s evolution exemplifies the transition from strings to definable things in search.
How Entities Work in Search
A. Entity SEO and Google’s core algorithm
Rather than treating the words in a search query as separate, fragmented keywords, Google’s core algorithm analyzes the overarching meaning and entities represented in the full query. It can identify each implied entity, meaning, and relationships to deliver relevant, context-mapped results.
Google’s language models are not limited to understanding entities in just a single language. They can detect and map entities while being language-agnostic – comprehending connections across queries in different languages.
This entity mapping extends beyond just the query itself. Google also analyzes the entire semantic web of online content, identifying the key entities and entity relationships on every page. It indexes this interconnected entity data, creating structured knowledge graphs that power semantic search.
B. Examples of entities in Google
We can see Google’s entity-mapping capabilities in actions across many of its products. In image search results, Google employs computer vision to detect and match visual entities in photos and graphics to the entity implied in the query text.
Google’s Discover feed curates content suggestions by mapping users’ interests, locations, and other context signals to relevant entities. It associates your entity footprint with content entities to surface tailored recommendations.
Even in regular web searches, the featured snippets, Knowledge Panels, and “People Also Ask” boxes are all examples of how Google injects entity information and relationships to answer queries—moving beyond just blue links directly.
C. Are entities a direct ranking factor?
While Google hasn’t outright confirmed entities as an official ranking factor, optimizing for entities inherently aids with many other confirmed factors that do impact rankings:
Relevance – Associating pages with accurate, context-mapped entities promotes higher relevance to search intent.
Semantics – Leveraging entities and structured data enhances semantic signals.
Context – Providing context through entity relationships creates more comprehensive, contextual content.
Furthermore, by structuring content as Google’s entity-first model prefers, pages are better aligned with its systems overall, improving visibility for relevant topics. So, while entities may not be a unique ranking factor per se, facilitating Google’s understanding of on-page entities is crucial for discoverability.
Optimizing Content for Entity SEO
A. Performing an entity audit
To optimize for an entity model, the first step is to identify the key entities relevant and valuable to your website, brand, industry, and target audience. Start by analyzing your current highest-trafficked pages to assess what primary entities Google already associates them with.
You can use tools, keyword data, and competitor research to unearth other potentially lucrative entities users search for in your space. Look at prominent entities across high-ranking pages, featured snippets, related entity suggestions, and other semantic signals.
Once you have a master catalog of target entities, the next step is to map out the rich entity graph – outlining how these entities are interconnected to form broad topics and concepts. Structure this entity ecosystem into defined entity sets, categories, and subcategories.
B. Optimization techniques
With your entity base established, you can start implementing optimization techniques across your site:
Internal linking – Strategically cross-linking between related entity pages to explicitly reinforce their semantic connections within your website’s knowledge graph. Descriptive anchor text helps search engines understand the entity relationships between linked pages.
Schema markup – Annotating on-page content using structured schema.org code tells Google precisely what entities are present and how they are classified/defined (e.g., LocalBusiness, Product, Person, etc). This removes ambiguity.
Knowledge graph creation – Moving beyond defining individual entities, you can build a centralized knowledge graph that coherently connects all of your website’s entities into an organized, multi-layered taxonomy. This holistically maps your entire entity universe for search engines.
Dynamic entity linking – More advanced techniques involve automatically identifying new entity mentions within fresh content and linking them to existing defined entity hubs to organically expand and maintain the semantic connectivity of your knowledge graph.
C. Going beyond SEO tool suggestions
While many modern SEO tools now offer entity optimization recommendations, their suggestions are often fairly rudimentary – simply identifying basic entity extraction opportunities from existing content.
For a truly robust entity-based strategy needed to capitalize on the future of semantic search fully, you must go beyond these out-of-the-box tool suggestions. Conduct comprehensive manual research and develop a bespoke content plan for your priority entity universe.
Lean on tools for foundational data, but overlay your layered conceptual strategy aligning content campaigns to focus on comprehensively establishing definitive entity authority – by creating the web’s canonical codified knowledge repositories for each of your core entity topics.
D. Entity-based SEO strategy
Keywords will always remain essential accessibility anchors, but the core focus of a modern SEO strategy should be entity-focused topic modeling and content mapping to achieve definitive entity authority.
For each priority entity you want to rank for, you need clear overarching content objectives like:
- Creating the most comprehensive, semantically rich content repository covering every aspect of that entity
- Earning contextual internal and external links to reinforce entity associations
- Implementing structured data to make entity properties clear to search engines
The goal is for your website to become the established semantic authority hub that Google unquestionably recognizes and associates with your targeted entity verticals and the interconnected entities within that entity’s knowledge graph.
The Future of Entity SEO
Impact of AI and natural language processing
Advancements in AI language models and natural language processing technologies will only accelerate search engines’ understanding of entities and how to map them to users’ naturally spoken/written queries.
As these AI capabilities improve, optimizing content for semantic entity comprehension rather than rigid keywords will become even more paramount. Strategies exploiting superficial keyword density tactics will be rendered obsolete.
Entities and AI go hand-in-hand in truly achieving human-like comprehension of language. Search engines will be able to understand multi-lay
