Why Entities Matter for SEO

Why Entities Matter for SEO

SEO pros should understand and leverage “entities” in SEO.

Working with entities in SEO is the foundation for a future-proof search strategy, especially with generative AI and ChatGPT.

What are Entities?

An entity refers to a record in a database, generally having a specific record identification.

In terms of search engine optimization (SEO), an entity is any distinct, singular, and well-defined thing or concept that search engines can recognize and understand. This could be a person, place, organization, event, or even an abstract concept like happiness.

Entities are important as they go beyond just words, having contextual meaning that search engines are used for delivering relevant search results.

SEOs confuse entities with keywords.

Entities and keywords seem similar, but they function differently in SEO.

Keywords are specific words or phrases searchers use in queries, while entities represent concepts and relationships with other entities.

For instance, the keyword [Apple] could refer to the fruit, the tech giant, or even an e-commerce website, but as an entity, which clearly means intended, it is based on the actual context.

“Entity-based SEO” as a practice helps search engines like Google connect user search queries with the correct meanings, which can improve your search engine results.

The Knowledge Graph

Knowledge graph is a semi-structured database containing entities.

The Knowledge Graph is generally the name given to Google’s Knowledge Graph, although thousands of others exist. Wikidata (which is itself a knowledge graph) attempts to cross-reference identifiers from different reputable data sources.

While the Knowledge panel, a specific representation of results from Google’s Knowledge Graph, is often shown on the right of the results (SERPs) in a desktop search, giving more details about a person, place, event, or other entity.

When you search for [Steve Jobs], the Knowledge Graph pulls up related entities such as [Apple Inc.] and [Pixer], helping Google deliver more complete and nuanced search results.

History of Entities in Search

Metaweb

Metaweb started building out a database called Freebase in 2005, which described as an “open, shared database of the world’s knowledge”, giving every “entity” (or article, to extend the metaphor) its own unique ID number and from there, instead of a traditional article in words, the system tried to connect articles through their relationships with other ID numbers in the system.

Wikidata

Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. It acts as central storage for the structured data of its Wikimedia sister projects, including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others, providing many other sites and services beyond just Wikimedia projects.

The content of Wikidata is available under a free license, exported using standard formats, and can be interlinked to other open data sets on the linked data web.

Entities in the Core Algorithm

Entities are primarily used to disambiguate ideas, not to rank pages with the same ideas.

When Google tries to serve up results to a user search, it aims first and foremost for an accurate answer.

Thus, Google spends considerable time converting text passages into underlying entities. This happens both when indexing your site and when analyzing a user query.

Language Agnostic

Entities are language-agnostic.

An image would be an obvious way to describe the Eiffel Tower since it is so iconic. It might also be a speech file or the official page for the tower.

These all represent valid labels for the entity and, in some cases, valid identifiers in other knowledge graphs.

Examples of Entities in Google

• Entities in image search. Entities are very helpful in optimizing images.
• Entities in Google Discover. Google provides a feed of interesting pages for users, even when they are not actively looking for something.

How To Optimize For Entities

Some Research from a Googler

In the year 2014, a paper came out that demonstrated Google were keen to separate out the ideas of using keywords for understanding topics vs. using entities. Dunietz and Gillick noted that NLP systems moved towards entity-based processing, highlighting how a binary “salience” system is used on large data sets for defining the entities in a document (webpages).

“Binary scoring system” suggested that Google decides to document either IS or ISN’T about any given entity. Later clues suggested that “salience” is measured by Google on a sliding scale from 0 to 1.

The paper is really helpful about where Google’s research thinking “entities” appearing on a page to “count” as being salient.

The paper cited:

• 1st-loc
• Head-count
• Mentions
• Headline
• Head-lex
• Entity Centrality

Entity Signals in SEO

Entity signals are used for understanding entities and how they are connected to other entities by using signals and context. Instead of matching keywords to queries, Google aims to understand the meaning behind a search query and its related entity.

Google uses these signals to comprehend the entity better and subsequently provide users with more accurate search results, understand the context and relevance of content.

Using Schema to Help Define Entities

Use of “about” and “mentions” schema helps search engines to disambiguate content.

For defining an entity using Schema Markup, you assign it a unique @id. This identifier functions as a Uniform Resource Identifier (URI), distinguishing the entity and making it referenceable across your site and beyond.

Schema Markup allows you to describe an entity using Schema.org properties, which define attributes and relationships between entities for enhancing search engines’ understanding of the entity