Entity Based SEO

 

In 2019, Google made a monumental shift in its search technology with the introduction of the BERT update. This significant change marked a departure from traditional keyword-centric search engine optimisation models towards a more nuanced approach using entity-based SEO and Natural Language Processing (NLP).

BERT, which stands for Bidirectional Encoder Representations from Transformers, enabled Google to better understand the context of words in search queries, thus enhancing its ability to comprehend the intent and the nuanced meanings of language.

This shift was not just a technical upgrade; it fundamentally changed how SEOs approached content, emphasising the importance of entities—distinct, identifiable objects and concepts—over mere keyword optimisation.

This evolution in search technology allowed Google to deliver more relevant and accurate search results by interpreting the relationships and attributes of entities embedded in content.

This transformation underscored the growing importance of semantic search and entity recognition in the SEO landscape, setting a new standard for content creators and marketers aiming to succeed in search engine rankings through entity SEO.

To learn some SEO strategy’s visit our other post  at strategies for SEO. In this post I will help you understand about Entities in SEO.

 

An Entity Vs Keyword: Entity Based SEO Strategy

 

 

Unlike traditional keyword-focused search engine optimisation (SEO), which revolves around optimising for specific words or phrases likely to be typed into a search engine, Entity-Based SEO centres on the concept of ‘entities.

These are distinct, identifiable objects or ideas that exist both physically and conceptually. Google and other search engines have shifted towards understanding and organising information around these entities rather than mere string of keywords.

This shift comes from the need to better understand search intent and contextual meaning behind user queries.

 

 

What are Entities in Entity SEO?

 

 

Entities are distinct, recognisable things or concepts that can be clearly defined. This includes people, places, organisations, brands, and abstract concepts.

In Entity SEO, entities help search engines decipher the context of content, improving the accuracy of search results. Search engine algorithms now emphasise entities over keywords to provide more precise information on search engine results pages.

For instance, when someone searches for “Apple,” search engines need to determine whether the user means the fruit, the technology company, or something else entirely.

 

 

Entity SEO Example: Related Entities

 

Consider “The Beatles” as an entity. This entity is connected with various attributes like their music genre, members of the band, and notable albums.

By recognising these connections, search engines can offer more relevant results for queries related to the Beatles, distinguishing between the band, the insect species, or even themed merchandise.

 

Connections Between Entities

 

Entities are interconnected through their attributes and relationships with other entities. Google uses these connections to form a Knowledge Graph, a vast network of entities, attributes, and the historical, conceptual, or cultural links between them.

For example, the entity “New York City” might be connected to “Statue of Liberty,” “Empire State Building,” and “Central Park.”

 

 

Understanding Entity Linking: Connecting Text to Knowledge

 

 

Entity linking is a crucial task in natural language processing (NLP) that involves identifying and linking textual mentions of entities (such as names of people, organisations, or locations) within unstructured text to their corresponding entries in a knowledge base or database.

The ultimate goal is to enrich the understanding and analysis of textual data by connecting it to structured knowledge resources.

In practical terms, entity linking operates in several key steps:

1. Mention Detection: The first step involves identifying potential entities within the text. This can include named entities like “Barack Obama” or “Apple Inc.”, which are typically proper nouns that refer to specific entities.

2. Candidate Generation: Once mentions are detected, a set of candidate entities from a knowledge base (such as Wikipedia or a custom database) is generated. This step aims to create a pool of potential matches for each detected entity mentioned.

3. Disambiguation: After generating candidate entities, the system needs to disambiguate which candidate best matches the context of the entity mentioned in the given text. This involves considering various features such as context clues, entity popularity, semantic relatedness, and entity descriptions.

4. Linking: Finally, the selected candidate entity is linked to the original entity mentioned in the text, creating a direct connection between the unstructured textual data and structured knowledge.

Entity linking finds applications in a variety of domains including information retrieval, semantic search, question answering systems, and knowledge graph construction.

By linking textual mentions to entities in a knowledge base, NLP systems can enhance search accuracy, improve information extraction, and facilitate deeper semantic understanding of text.

The accuracy and effectiveness of entity linking systems depend heavily on the quality of the underlying knowledge base, the sophistication of the disambiguation algorithms, and the ability to handle ambiguity and context in natural language.

Ongoing research continues to refine these techniques, aiming to improve both precision and recall in entity linking tasks across different languages and domains.

 

 

The Google Knowledge Graph and Entities

 

 

Google’s Knowledge Graph is based on data that Google uses to enhance its search engine’s results with information gathered from a variety of sources.

This information is presented to users in a box next to the search results. These results are generated based on entities and their connections, significantly affecting how content is recognised and ranked.

 

 

What Is Entity-Based SEO?

 

Entity-Based SEO involves optimising your website’s content around entities recognised by Google and structuring information in a way that search engines can readily understand and process.

This means not only including clear mentions of relevant entities but also providing comprehensive information that relates these entities to each other in meaningful ways.

 

 

Why do Entities Matter for SEO

 

Entities contribute to the semantic understanding of content by search engines. They help in categorising and indexing content more effectively, allowing for more precise and relevant search results.

By focusing on entities, websites can improve their relevance and authority, as well as enhance user engagement and satisfaction.

When done right, your SEO helps search intent and Googles understanding of your content and connects the dots with your business, website and Google knowledge graph.

 

 

Semantic Search vs Entity Search engines

 

 

Semantic search refers to the understanding of the intent behind a user’s search query, rather than just the literal words typed. In contrast, entity search involves identifying and retrieving content that mentions specific entities related to a query.

Both approaches aim to improve the accuracy and relevance of search results, with entity search providing a more structured exploration of data.

 

 

Unstructured Data References

 

In many cases, content on the web is unstructured—plain text without explicit markers indicating how bits of information relate to each other. Entities help convert this unstructured data into a structured format that search engines can understand and utilise more effectively.

 

 

How Do Entities Fit in Structured Data and Schema Markup?

 

 

Using structured data and schema markup, website owners can explicitly indicate which parts of their content represent entities.

This markup directly communicates to search engines the specifics of entities present on a webpage, such as a person’s name, a product’s price, or an event’s date and location, thus enhancing the precision of entity detection.

 

 

Entities and NLP Words

 

Natural Language Processing (NLP) technologies enable search engines to understand the context in which words and phrases are used, improving their ability to identify entities and their relationships within the content. NLP is crucial for parsing human language in a way that aligns with how humans understand and use it.

 

 

Importance of NLP in SEO

 

 

NLP enhances the capability of search engines to interpret content’s meaning and relevance to specific search queries. It plays a vital role in content ranking, especially in determining how well a piece of content satisfies user intent and matches with related entities.

 

 

How to Use Entities on Your Website

 

To leverage Entity-Based SEO:

Identify relevant entities associated with your business, industry, or content. Carry out an entity audit on your market and competitors.

Use schema markup to clarify the entities on your pages.

Create content that thoroughly explains and links between these entities, providing a rich context that search engines can index and understand.

 

 

Start with an Entity Audit

 

 

To make the most of entity-based SEO, first ensure your website accurately reflects the entities linked to your brand.

Here’s what you do first: an entity audit.

This audit is about reviewing your entities, comparing them to your competitors’, and confirming you’ve captured the best entities for your site.

Wondering how to find the right entities for your website? Thanks to advancements in AI, researching entities has become simpler.

John Butterworth, Head of SEO, explains how he uses AI to gather relevant entities:

“Since ChatGPT launched, it’s revolutionised how I identify and incorporate relevant entities into my content. While tools like SurferSEO are great for identifying NLP terms, they only suggest terms based on what’s already used by top-ranking pages. This means they might miss out on important entities not yet used by competitors.”

Here’s where ChatGPT helps. It can uncover hidden entities, which could enhance your content significantly. For example, using this custom prompt he designed:

“I will provide you with a keyword. I want you to list semantically relevant entities concerning the provided keyword based on: people, competitors, institutions, concepts, locations, occasions, tools, and future predictions. The keyword is: [enter your keyword].”

 

 

Boost Your Knowledge Graph Card

 

 

Google launched its knowledge graph in May 2012, promoting it with the phrase, “Things, not strings.”

The knowledge graph is a vast database that enables Google to provide instant answers to queries about real-world entities. It’s also made semantic SEO crucial because it helps increase your sites visibility, user experience, and search performance.

To enhance your knowledge graph, use Structured Data:

Structured data is a format to organise information on your page, making it easier for search engines to categorise and display in search results. Schema markup is one effective way to apply structured data. It helps describe your site’s data better, making it easier to locate in search results.

You can find various entities on schema.org to add to your site, including:

• Creative works like books, movies, and music
• Embedded objects such as audio, images, and videos
• Events
• Health and medical types
• Logos
• Names
• Organisations
• Local Business
• People
• Places, local businesses, restaurants
• Website
• Products and offers
• Reviews and ratings
• Actions, and more

Using structured data not only enhances your knowledge graph card but also improves your search engine rankings.

Brady Kirkpatrick, Founder of Gunmade.com, shares his experience:

“Before using structured data at GunMade .com, we struggled to rank well. But after applying structured data to highlight prices and optimize content around entities like gun manufacturers, Google better understood our content. This led us to move from pages 2 or 3 up to consistently ranking on page 1 for many keywords.”

Remember to read Google’s guidelines on necessary structured data features and use the Markup Testing Tool to check your markups.

Additionally, consider leveraging open data sources like Wikipedia and DBpedia. These platforms are often used by Google and can significantly boost your site’s reliability and visibility. If you don’t yet have a Wikipedia page for your business, creating one can be beneficial.

 

 

Monitoring Entity-Based SEO Performance

 

 

Track the performance of your Entity-Based SEO strategy through tools like Google Analytics and Search Console, focusing on metrics like page views, bounce rate, and conversions. Adjust your strategy based on these insights to better target and engage your audience.

 

 

Tools to Help Find Entities

 

To effectively identify and analyse entities for SEO purposes, there are several specialised tools available that can significantly aid your efforts:

1. Topically – This tool leverages data from Google Images and search modifiers to help understand entity relationships and user intent. It offers a hierarchical view of these relationships, providing insights into how entities are connected and how they can be utilised in content for better SEO performance.

2. InLinks – This tool focuses on content optimisation through entity, keyword, intent, and gap analysis. InLinks generates detailed content briefs that include topic maps and suggested content structures based on the entities and concepts surrounding your target keywords.

3. Entity Analyser – Provides a suite of tools designed to enhance your SEO strategy by helping you explore entities related to your content and industry. Features include the ability to generate structured entity schemas for your web pages, which can improve how well search engines understand and rank your content.

4. WordLift – Offers a free tool for extracting entities using AI. It analyses content to identify all relevant entities and links them to their corresponding Wikidata descriptions, enhancing SEO benefits through better content understanding and AI-enriched schema markup.

5. Zizta’s Entity Explorer – This tool excels in researching and identifying the best entities and phrases that align with user intent and topical relevance. It provides features such as keyword and entity density analysis, optimising your content visibility and relevance in search engine results.

These tools cater to various aspects of entity-based SEO, from content creation to technical SEO enhancements, making them invaluable resources for anyone looking to optimise their online presence through a deeper understanding of entities and their impact on search rankings.

 

 

Helping Your Google Presence

 

 

By integrating Entity-Based SEO into your website, you not only improve your visibility in search engines but also provide a richer, more engaging user experience.

One thing to consider, is using your brand (business name) and associating entities with your brand name to build a relationship online between the two.

This is an SEO strategy I have used at RedKite SEO for the last 3 years to help secure a business and its connection in its market for digital marketing and getting relevant website traffic from organic search results.