Entities are a bit of a mystery. They've been around for years now and have influenced many aspects of search, but they are rarely talked about. I guess part of the reason is that there is not much solid information on entities and notoriously ambiguous Google patents are not much help. But the other part is that, even if you understand entities, it is unclear if they can be used for SEO.
In this article, I have gathered what little information we have about entities and did my best to translate it from patent language to human language. And I've managed to discover a few SEO tactics along the way.
What are entities in SEO?
Let's turn to one of Google's patents for the official definition of entities:
An entity is a thing or concept that is singular, unique, well-defined, and distinguishable. For example, an entity may be a person, place, item, idea, abstract concept, concrete element, another suitable thing, or any combination thereof. Generally, entities include things or concepts represented linguistically by nouns. For example, the color "Blue," the city "San Francisco," and the imaginary animal "Unicorn" may each be entities.
In fewer words, an entity is anything notable enough for users to search for it by name. For example, I'm not well-known enough (yet) to be an entity — I'm just one of many writers on the topic of SEO and no one is searching for me by name. But take Bill Slawski — he has earned his entity status by being a widely recognized expert on Google patents, by being linked to, mentioned, and interviewed all over the web.
How does Google find entities?
Google is building its entity database using two distinct processes: copying existing entities and discovering new ones. Right now, Google is mostly copying its entities from existing knowledge bases, like Wikipedia and IMDb. It allows Google to grow their own database quite quickly, but to keep it kosher because they only draw from a few trusted sources. The drawback is that those knowledge bases can be slow to include new entities and to update old ones, so Google is at risk of not serving the most relevant content.
To combat this issue, Google has patented a few methods for discovering new entities from unstructured data available on the web. One method suggests using known entities to see if they are connected to any unknown entities, either through syntax or by frequently appearing together within the same document. For example, if many documents say "Andrei Prakharevich is a writer at Link-Assistant", which is a known entity, then Google might eventually wonder whether Andrei Prakharevich could be an entity.
Another method suggests measuring entity value against the size of its field, i.e. it should be easier to become a notable entity in a narrow field than in a broad one. For example, it would be quite difficult for a writer to become an entity within the whole field of SEO, but much easier to become an entity within a subcategory of SEO.
Today, Google is getting better and better at understanding natural languages. That is the way we humans use and interpret language without even thinking about it.
This is not the case for search engines such as Google, as they can only understand language in a very rigid way, word by word, meaning by meaning. Not until entities for SEO began. Whereas we the human users, can process the meaning behind each word or each sentence fluidly based on our own prior knowledge and the context presented.
Importance of entities in SEO
Google defines an entity as: "A thing or concept that is singular, unique, well-defined, and distinguishable." This is a very broad definition, but it is important within the realm of SEO. Unfortunately, Google does not publish the exact details of their use of entities, so information largely gleams from patents, the information provided by Google developers, and experimentation.
Though the exact workings of entities are unknown, there are methods to help Google understand the content on your website better. This form of SEO involves adding structured data to pages. There are several different formats that can be used to provide structured data, such as JSON-LD, microdata, and RDFa. These different formats all provide a schema of the pertinent information on a page.
Structured data can specify the type of content on the page (e.g. a recipe, a news article, a movie review), any people mentioned (people within the page or the author of the page), when the page was published, and other miscellaneous data. The information is highly dependent on the page. As an example, a page containing a recipe may include the prep time, cooking time, ingredients, recipe yield, and ingredients within its structured data.
Google does not guarantee that provided structured data will be used in search results. It ranks this information on 3 major factors: content, relevance, and completeness. Content that is up-to-date and high quality, which is highly relevant to the page content and is not missing any pertinent information, will be more likely to be taken into consideration by Google and included in search results.
How Google uses entities in search
In search, an entity can seem similar to a keyword at first glance, and they both relate to the content on a webpage. However, where a keyword is bound by language, an entity can be anything individually distinguishable: a brand, a person, a place, a concept, and more.
Entities are used to understand concepts and their relations in content. For example, someone may search for "presidents of the United States of America". All sorts of the content may be returned for this search term. Each individual president may have a wealth of information stored about them. This information may be spread out across many mediums - pictures, videos, audio, and text data. All of this information may be considered under the umbrella entity of each individual president.
By considering data in terms of entities, Google can gain a deeper understanding of content and provide more relevant search results. In this way, each entity can be considered a node, and these nodes are linked together by relationships. Information like birth dates, death dates, notable accomplishments, other presidents, and world leaders may all be related to these nodes in some way, and this information can be included in search results.
Google’s Knowledge Graph as an example of the application of entities
Google's application of this concept is called the Google Knowledge Graph. Unfortunately, Google does not publish information about the inner workings of the Knowledge Graph, so publicly available data regarding the exact implementation of Knowledge Graph and how it relates to SEO is limited.
Google Knowledge Graph information is used to provide extra content to search results. So, for example, search results for "Thomas Jefferson" would include a box with pertinent data about him: images, notable accomplishments, a short blurb, and other biographical data, like birth and death dates. Related searches, such as other presidents and statesmen from his era, are also listed.
This information is drawn from the Google Knowledge Graph. Thomas Jefferson is an entity, and images, biographical data, and related searches all form part of the entity. While its exact implementation is unknown, there are some clues to how it works.
A concept called 'co-occurrence' is important in how Google forms relationships between different entities. Co-occurrence refers to how frequently two different entities are linked together in some way, such as how often two different names are both mentioned together. In this way, Google may 'learn' about the relationship between a president and vice president by how frequently their names are mentioned together.
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