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by Wayne Smith
The entity-relationship model is fundamental to how language is processed and how search engines look at the page for relevancy. Instead of looking at word frequency; Content can be evaluated using related entities, and for the citations to closely related terms.
The "Things, not Strings" or the begining of entity based indexing began when Google introduced the Knowledge Panel, but Bert was the major shift for on-page SEO.
"Google’s Knowledge Graph isn’t just rooted in public sources such as Freebase, Wikipedia and the CIA World Factbook. It’s also augmented at a much larger scale—because we’re focused on comprehensive breadth and depth. ... And it’s tuned based on what people search for, and what we find out on the web."
The idea that Google was using a latent semantic index roughly appeared within the same period. The major difference is that LSI uses a specific set of related words. AI determines the relationship between entities using algorithms, can be more detailed, and flexible to learn new relationships.
Almost all nouns (persons, places, things, or concepts) are considered entities. However, in search optimization, entities represent the subjects of sentences or the main topics of a page. For SEO, these entities are specifically those that people actively search for, and the semantically related entities.
Finding Related Google Entities
Search engines, and Google in particular, have many algorithms. Some people may think that these algorithms can actually read and understand the content. Then, on an emotional judgment, decide which content is best. Search engines actually just scan the content and sort pages based on mathematical algorithms.
An entity can be a person, place, thing, concept, or thing owned or created by a person ... Website, Business.
As an example let's consider an entity you are an authority on ... yourself.
You have a name, address, date of birth, a profession, and a employer ... A dataset with these data points can be built using the schema naming convention.
When somebody searches for John Doe if they include an indication of which John Doe (IE John Doe the expert) the search engine identifies two entities, the name and the jobtitle. Looking at the data Google has collected for the entities Google can use the associated indexes for these entities, which are pre-sorted based on breadth of knowledge of interest. Find the highest ranked URL that is common to both indexes -- Instead of treating it as a keyword or unknown entity and looking for page of names using Keyword density or Keyword predominance because it is the page title.
The entity based page is argued to be a better result than the page using a matching title and keyword density. Entity based is looking at authority sites to build its dataset, then ranking pages using both indexes.
When creating content and schema it is important to use valid entities ... o*net online provides a list of valid job titles, occupations, and their responsibilities. "Expert" is not in the public authoritive database of job titles! John needs to pick his title from what is available from o*net. Otherwise the jobtitle will be ignored -- valid information is information that can be verified.
Natural Language Algorithms are able to find the information but the processing introduces the need for additional cost and time, but using schema effectively can level the playing field between sites with more or less Crawl/AI budget.
The website schema can be used by Google in creating or filling in the Knowledge Panel on Google search. A knowledge panel about the site will only be created when Google has confidence the intent of the search is for information about the site, and may not be created if search volume is low.
Entity Content Silos vs Evergreen SEO Silos
An SEO Sile would be a group of pages related to a keyword with a hierarchical link structure to a central parent page. They provide easy navigation to all the sub-pages. Content silos can be stacked linking to additional silos.
Skyscraper Page to Semantic Topical SiloConverting skyscrapers is akin to the avalanche SEO strategy; but the keywords or terms are based on the actual terms the site can, or should I say does, appear for in the SERPs. The site over a period of time continues to update and provide a gain of knowledge on the topic -- as an authoritative resource for the topic -- resulting in an upward trend in Google related to the subject matter for the site.
Entity-based AI search optimization
AI search optimization is optimizing for entity algorithms (Open AI, Google generative search, and voice search). These systems use the entity data to find facts and documents.
Entity-based AI search or (Open AI, Google generative search, and voice search), systems use the entity (relationship model) data to find fact and documents. Using schema is an SEO best practice, as search engines can use natural language which is a by-product of AI.
Why a brand entity should be used as a domain name
A brand has an identifiable relationship model with all related entities services or products it offers. When a brand comes into existence it is a clean slate, which can freely identify its products without the history or baggage an already existing term has.
When a business is created and structured as an entity it becomes relevant to the business location and products or services it offers.
Breadth refers to how wide or how many sub-topics a specific entity or word has. A search for tools has many types of tools ... Google will try to clarify which type of tool is being searched for by providing a "types of" search panel when searching for that word. A search for Vienna sausage does not have sub-types -- depth relevance becomes more important than breadth ... Google will provide a shopping panel.
Entity-based SEO is building on the breadth, which wants the relationship between the query and related entities. When breadth is not useful for a search query, depth remains relevant. But, it is important to note these are distinctly different content types and content strategies based on completely different algorithms.
Bottom of the sales funnel when somebody is looking to purchase a product ... content is depth-based. Top of the sales funnel or "awareness" when somebody is window shopping ... content is breadth-based.
It is generally understood by Tenured SEO professionals that search engines improve and sometimes revolutionize how they retrieve and rank documents. A tenured SEO professional, knowing that algorithms change, adapts to new algorithms.
Keywords can be ambiguous and may reference more than one subject. An entity, on the other hand, references a single subject. Language translation using entities instead of keyword-by-keyword, provides better results. Entity-based algorithms within search makes voice search, chatbot, and AI search possible. Using the breadth of the entity the algorithm is able to find a matching document better than trying to find a document with both keywords.
Because entity-based algorithms are able to provide better results they are going to remain and have a greater effect on search and SEO into the future.
Full-stack SEO has many moving parts, and it takes time and effort to understand the nuances involved. Solution Smith takes care of the overhead costs of digital marketing, saving time and effort.
A search algorithms is based off of mathmatics and ranking factors must be quantifiable or countable ... factors that are only true or false can be used for quality indicators to determine if a site will be listed or buried.