What is an Entity? Definitive Guide

The audience I want to target through this article is those who are in the SEO field or want to come into this field, but they see Google’s updates almost every week and are afraid of uncertainty; they understand the only way to become the top 1% in SEO is to learn semantic SEO, but they feel difficult to understand these terms and concepts.

In 2023, Google released massive updates prominently in the last quarter of the year.

The SEOs were still dealing with the October core update when Google launched the November update, so now it’s the most uncertain era of SEOs. To stay ahead, you must work aligned with the Google algorithm, not against it.

How do you align with Google’s algorithm?

You align with Google’s algorithm by learning,

• How does the Google search engine work?
• Which patterns does Google follow?
• How do they render your website?

SEO’s job is not to trick search engines but to help search engines easily crawl your website without using much crawl budget.

Let’s start with our first topic of semantic SEO is “Entity”.

What is Entity

A simple definition of entity according the google meaning is  “a thing with distinct and independent existence”

Example: Church and empire were fused in a single entity

According to Google, an entity is anything that is “singular, unique, well-defined, and distinguishable.(source)

Entities can be:

• Person
• Place
• Organization
• Thing
• Etc.

Entities are not the same thing as a keyword; Entities are machine-readable, and Keywords are not.

In 2012, google introduced a knowledge graph and moved towards “things, not strings.”

This shift from “strings” to “things” means Google now focuses on understanding the entities and concepts behind search queries rather than just matching keywords.

For example, when you search “where is headquarter of apple?” and show the result “Cupertino, California, United States.” 

In this case, Google looks for web pages that contain the keywords “headquarter” and “apple” to provide a search result.

With the introduction of the Knowledge Graph, Google is now able to understand the entities or “things” that are represented by keywords in search queries. This allows Google to provide more comprehensive and informative search results.

For example, if you search for “where is headquarter of apple?” Today, Google will display a Knowledge Graph panel on the right side of the search results page.This will contain information about Apple. Inc. Moreover, because its headquarters is in Cupertino, google shows on the left in the knowledge panel information about Cupertino, such as its population, Area, and Unemployment rate. Google will also display links to other relevant web pages, such as Apple’s official website. Inc. and the Wikipedia page for Apple. Inc.

Is it necessary that an entity must be alive?

To be considered an entity, it is not necessary to be alive. Entities can be physical objects, such as a table or a car, or intangible concepts, such as love or democracy. They can also be events, such as the Super Bowl or the World Cup.

An entity is simply something that exists and can be identified. It does not have to be alive or even physical.

For example, the entity “Steve Jobs” is not alive, but it is still an entity because it is a real object that exists in the world.

What is meant by entity?

Let’s put it: there are hundreds of languages worldwide. Suppose Google tries to store every entity language variation in its database. In that case, it takes a lot of storage, so instead of keeping the entity and its hundreds of variations in different languages, Google gives every entity a unique ID, just like you have a unique ID of a citizen in a particular country, so there are hundreds of people who names are John. Still, your National identification number is unique in the world, which helps all worlds to see you as a unique entity, just like your fingerprint is unique.

For Example: Apple writes in different ways in different languages.

• English: apple
• Chinese (Mandarin): 苹果公司
• Japanese: 北京
• Korean: 애플
• Russian: Эпл

So now google stored apple or 苹果公司 with might be with unique ID 9876543210

Entities with a Sense of Place

Search engines personalize their results based on location.

Search engines change is results based on location

When you search for “Bat”, you’re presented with 3 distinct entities, each with its own unique characteristics.
1. Bat (animal)
2. Bat (cricket bat)
3. Bat (British American Tobacco cigarette company)
It depends on your interest, location and historical data that which entity Google shows you prominently

For example, when I search for “Bat” from Pakistan, it shows me all 3 entities.

but when I use a VPN and select my country, the USA, it displays Bat (animal) prominently.

Interestingly, when I searched Bat established United Kingdom, my country, it prominently showed Bat (British American Tobacco cigarette company).

I hope you got the idea of how location, interest, and historical data play significant roles in search engines; in this way, search engines try to answer each query individually, not just rank one type of entity and show it worldwide.

How Google distinguish these same spelling entities?

The patent on named entity disambiguation was filed in 2006, The named entity disambiguation patent is a way for computers to make sense of text by identifying and classifying named entities, such as people, places, and things. This allows computers to extract information from text more accurately and to understand the meaning of text better.

What is meant by disambiguation? 

Sometimes, a word or sentence can have more than one meaning. This is called ambiguity. Ambiguity is when a word or sentence is unclear, and disambiguation is making it clear. Let me give you an example so it makes it more transparent to you. 

Example:

Sentence 1:

This sentence is ambiguous because the word “apple” has two meanings:

1. A technology company known for its electronic products.
2. The fruit that grows on apple trees.

Without more context, it is impossible to know which meaning of “apple” is intended.

Sentence 2:

This sentence is Clarified because it clarifies that the speaker is referring to the first meaning of “apple”. The word “product” is a compound word that specifically refers to the apple (technology company).

The million-dollar question is: how does Google tell apart entities with the same name?

The answer is knowledge base

What is the knowledge base?

A knowledge base is a collection of organized information on a specific topic, like a smart database. It’s a go-to resource for quick answers and insights.

Let me give you an Example: You can picture a fruit market with baskets of apples, oranges, bananas, and other items. The fruit market here refers to knowledge bases and baskets, which represent the neat and clean way knowledge bases store information about each entity.

Now understand named entities with little bit deeper level.

Named entity

Now you have some knowledge about entities so let’s move into our next topic that is named entity. 

When you search for apple, Google shows you apple’s

• Age
• Net worth
• Picture
• latest news

• as you scroll down, you see a Wikipedia page of Bill Gates (google uses Wikipedia to understand and distinguish between entities). On the left, there is a knowledge panel that shows when he was born, his worth, his children, an organization he founded, spouse, height, and award, and all main social media profiles of Bill Gates,

• and at the right, top stories regarding Bill Gates and some more related entities in Google Knowledge Panels, “People also search for” section.

Why?

The “People also search for” offers links to other entities that users commonly look for in the initial search. These suggestions are generated based on patterns observed in search queries, indicating what users naturally seek in connection with the original entity.

when you search for “Bill Gates,” the Knowledge Panel might include a “People also search for” section with suggestions like “Melinda French Gates,” “Steve Jobs,” “Jeff Bezos,” and other figures commonly associated with Bill gates.

Why Broaden Results for Named Entities?

When someone searches for information, they often just want to answer a specific informational need, such as the date a specific event happened, or a transactional need such as downloading some software or making a purchase. But a searcher may want more information than just a single search result, and maybe try to explore a topic that they don’t know much about. (Source). 

Why are entities important?

Entities are important because they provide a way to organize and store data in a structured and efficient manner. By organizing data into entities, we can make it easier to find, access, and update information.

Another point is that entities are important because Recognizing entities is essential for extracting relevant information from texts. For example, in a news article, identifying entities like people, organizations, and locations can help summaries the key points and events.

For Example

“Apple announced that it has acquired a new startup specializing in artificial intelligence.”

In this sentence, entities play a crucial role in understanding the meaning:

“Apple”: This entity refers to the company, not the fruit. Recognizing this entity is essential for understanding that a corporate action is taking place.

“startup specializing in artificial intelligence”: This entity provides additional information about the type of acquisition. Identifying this entity is vital for grasping the specific focus of the business being acquired.

“artificial intelligence”: This entity represents a technology or field. Recognizing it is important for understanding the context of the startup and the kind of expertise or products involved in the acquisition.

“acquired”: While not a traditional named entity, the verb “acquired” implies a specific action has taken place, indicating a change in ownership or control. Understanding this entity is crucial for comprehending the nature of the news.

How Google uses entities?

• Google uses entities to personalize results for an individual based on its interest, location and historical data.
• Second is google use Knowledge graph to organize entities, providing context and relationships between people, places, things, and concepts. It enriches search results with detailed information.
• Entities help Google interpret user intent behind queries, distinguishing between different entities based on user past interest.

For example

The search engine shows apple(technology) when you search for “apple use”.

but when you search “apple uses”, it shows apple(fruit). 

How to optimize around entities?

Now, let’s give you a practical example of how you optimize content around entities.

At this time, we take the example of an iPhone 13; imagine you are running an e-commerce store and selling a iPhone 13.

How do you optimize your e-commerce store around entities?

For example

Instead of simply saying, “This iPhone 13 camera takes great photos,” you could include specific details like its 24-megapixel sensor, versatile lens type, and 4K video recording capabilities.

This approach has two benefits:

• It helps users to quickly and easily find the information they’re looking for.
• It helps search engines better understand your content and connect it with relevant entities.

So when the user searches for a 24-megapixel sensor iPhone 13 camera, google quickly finds you as the most relevant page, and it shows to the user.

Entity around voice search

Voice search entities are also used by businesses to improve their visibility and reach in voice search results. For example, an Apple Store can optimize its website and Google Business Profile to include relevant entities, such as “Apple Store,” “electronics,” and “technology.” This will help to ensure that the Apple Store appears in search results for voice queries such as “Find me an Apple Store near me.”

Use structure data markup for entities

What is structured data markup?

Structured data markup is a way to add extra information to your website that makes it easier for search engines to understand. It’s like adding labels to your website’s content, so that search engines can quickly and easily identify what your content is about.

What Does schema markup look like?

We will use the same iPhone 13 example.

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "iPhone 13",
  "description": "The iPhone 13, featuring advanced technology and design for a seamless user experience.",
  "image": "https://example.com/iphone-13.jpg",
  "brand": {
    "@type": "Brand",
    "name": "Apple"
  },
  "offers": {
    "@type": "Offer",
    "price": 1199.99,
    "currency": "USD"
  },
  "specifications": {
    "storageCapacity": "Varies",
    "display": "OLED Super Retina XDR",
    "camera": "Advanced multi-lens system",
    "videoResolution": "4K"
  }
}

As you can see in the above piece of code, it can help search engines easily understand what the page is all about and reduce the crawl budget.

Structured data helps identify and define these entities in a way that search engines can easily recognize.

For example

If a web page has information about a iPhone 13 , structured data can mark up details like the “name”, “manufacture”, “release date”.

Take a look at the below schema markup code.

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "iPhone 13",
  "manufacturer": {
    "@type": "Organization",
    "name": "Apple Inc."
  },
  "Releasedate": "2021-09-14",
  "category": "Smartphone"
}

That’s how you can use schema markup and help google to easily find entities.

Common mistakes to avoid

Here are some examples of the mistakes listed above, with proper nouns:

Using the wrong entity type:

Instead of using the Person entity type to represent a person’s name, using the Text entity type.

Right

{

  "@context": "https://schema.org",

  "@type": "Person",

  "name": "John Doe"

}

Wrong

{

  "@context": "https://schema.org",

  "@type": "Text",

  "name": "John Doe"

}

This is a mistake because Person is the correct entity type for representing a person’s name.

Using ambiguous entities:

Instead of using the entity iPhone 13 to represent a specific model of smartphone, using the entity electronics.

Right

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "iPhone 13",
  "description": "The latest smartphone manufactured by Apple."
}

Wrong

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Electronics",
  "description": "The latest smartphone manufactured by Apple."
}

This is a mistake because Honda Civic is a more specific and unambiguous entity than Car.

Using too many entities:

Representing a product’s name, brand, model, and category using separate entities, instead of using a single entity to represent the entire product.

Right

{

  "@context": "https://schema.org",

  "@type": "Product",

  "name": "iPhone 13 Pro Max",

  "brand": "Apple",

  "model": "iPhone 13 Pro Max",

  "category": "Smartphone"

}

Wrong

{

  "@context": "https://schema.org",

  "@type": "Product",

  "name": "iPhone 13 Pro Max"

}

This is a mistake because it is more efficient and easier to read to use a single entity to represent the entire product.

Not using entities consistently:

Representing a person’s name using the Person entity type on one page, and using the Text entity type on another page.

Right

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Apple Inc."
}

Wrong

{
  "@context": "https://schema.org",
  "@type": "Text",
  "name": "Apple Inc."
}

This is a mistake because entities should be used consistently throughout your data. This will make your data easier to read and understand.

By avoiding these mistakes, you can make your data more accurate, accessible, and useful for both humans and machines.

Figuring Out Why Certain entity About Something Are More Important in Certain Situations

There are 3 main attributes that play a crucial role.

• Prominence attribute 
• Relatedness attribute
• Popularity attribute.

In that case we take movie entity in this example.

Entity: Apple

Prominence attribute:

Example: In the context of product reviews, attributes like “performance,” “design,” and “user interface” might be more prominent as they significantly contribute to the overall assessment of an Apple product.

Relatedness attribute:

Example: In a discussion about smartphones, attributes like “operating system integration” and “app ecosystem” are highly related to the contextual domain.

Popularity attribute:

Example: Synonyms and query patterns for the Apple iPhone might include terms like “iOS,” “Apple ecosystem,” and “sleek design,” which are popular in search queries related to its features.

Total Search Demand: Analyzing search demand might reveal that user interest in “latest Apple products” is higher than other attributes.

That way, you can prioritize certain attributes based on certain circumstances.

Final Thoughts

Learning semantic SEO, focusing on entities rather than keywords, learning and implementing tests, revising, and repeating are the only ways to move forward in 2024.