Entity linking is the process of connecting the terms or entities mentioned within your content to unique, well-defined corresponding entities found in external authoritative knowledge bases, such as Wikipedia, Wikidata, or Google’s Knowledge Graph.
By associating these entities with external authoritative knowledge bases, you ensure that their meaning is clear and disambiguated for machines and search engines.
The external knowledge bases you link to can vary by industry or content type. For example, in the medical or finance industries, specific glossaries or governing bodies may provide the best definitions for entities within your content.
Entity linking can be performed within your Schema Markup using Schema.org properties. In the context of Schema Markup, it often involves “connector” properties, like sameAs or mentions, to connect an entity to a corresponding entity.
Linking your entities enhances how search engines process and contextualize your content, leading to better visibility and more accurate search results. Without entity linking, ambiguous terms might confuse search engines, potentially harming your page’s relevance, ranking, and traffic quality.
For instance, if your page discusses “Jaguar” (the car brand), your JSON-LD markup with entity linking might look like this:
{
"@context": "https://schema.org",
"@type": "Brand",
"name": "Jaguar",
"sameAs": "https://en.wikipedia.org/wiki/Jaguar_Cars",
"sameAs": "https://www.wikidata.org/wiki/Q30055",
"sameAs": "kg:/m/012x34"
}
This explicitly informs search engines that your page refers to the car brand Jaguar, not the animal. By integrating entity linking into your Schema Markup, you make your content more accurately understood by search engines. When search engines correctly interpret the context of your content, they connect it with the right audience. For businesses, this means better engagement and higher-quality traffic.
Why is Entity Linking Important?
For enterprises, entity linking is a strategic advantage that enhances content clarity, ensures contextual accuracy, and improves engagement across both human and AI-driven systems.
1. Enhances Search Engine and AI Understanding Through Disambiguation
Branded content often includes specific product names, services, or proprietary terms that might not be universally recognized. Without entity linking, search engines could misinterpret these terms or fail to associate them with your content. By linking these entities to external authoritative knowledge bases, like Wikipedia or Wikidata, enterprises can ensure their content is understood in the correct context.
For example, if your content mentions the term “Apple,” you should use entity linking to clarify if you are referring to the tech giant or the fruit. This prevents confusion and reinforces your content in relevant searches.
This process of disambiguation not only improves search engine accuracy but also supports AI-driven systems that rely on precise context to function effectively.
As AI technologies evolve, their ability to understand user intent depends heavily on clear and structured content. Entity linking plays a key role by connecting specific terms and concepts to their correct meanings, enabling AI systems to process and respond to information accurately.
For instance, a chatbot answering customer questions about products can benefit immensely from robust entity linking. If the chatbot references a structured knowledge base where all product names, categories, and attributes are linked to their corresponding entities defined within a knowledge base, it can handle vague or ambiguous user queries more effectively. This ensures a seamless user experience where AI provides accurate, contextually relevant responses.
When entity linking is applied effectively, it lays the groundwork for creating robust, reliable AI systems.
2. Increases Content Discoverability
For enterprises with diverse content, products, or services, content discoverability is crucial for driving traffic and conversions. Entity linking helps search engines connect content to relevant queries more efficiently, making it easier for users to find what they need and click through to pages in search that are closely aligned with their search intent.
At Schema App, we implemented entity linking at scale on our customer, Marshfield Clinic’s physician pages. As a result, their pages were ranking for more relevant keywords and they saw a 32% increase in click-through rates to their physician pages.
During another experiment, one of our customers had a set of location-based landing pages for various audiences across the US. We implemented place-based entity linking on 11 test pages and kept 4 control pages to compare the results. The test sites saw an increase in the number of queries containing the state name and ‘near me’, leading to a 46% increase in impressions and a 42% increase in clicks for non-branded queries.
By clarifying the locations serviced on the site, this customer’s pages showed up for more location-based queries.
Read Measurable Impact of Scaling Entity Linking for Entity Disambiguation to learn more.
3. Ensures Relevance in a Multilingual and Multiregional Context
Global enterprises targeting audiences across different languages and regions often face challenges with entity ambiguity. Entity linking resolves this by ensuring the correct context is maintained across languages and regions.
For instance, the term “football” might refer to soccer in Europe but American football in the USA. By leveraging entity linking, businesses can disambiguate terms in their content to match the regional context. Continuing our example, linking “football” to FIFA or the NFL ensures that search engines and users understand whether the content pertains to soccer or American football. This approach avoids confusion, maintains consistent brand messaging, and ensures the content resonates with the intended audience in each region.
Understanding the importance of entity linking and its benefits is just the first step—now, let’s explore how to implement it effectively using Schema Markup to ensure your content is both machine-readable and contextually accurate.
How to Implement Entity Linking Using Schema Markup
Entity Linking on your website involves a few key steps to ensure clarity and consistency for both search engines and users. Here’s how to do it manually:
1. Identify Key Entities
Start by pinpointing the terms (entities) in your content that require disambiguation or added context. Focus on entities that have distinct identities, such as places, organizations, names, and other terms that could have ambiguous or multiple meanings.
For instance, if you’re writing a blog post about luxury car brands and mention “Jaguar,” this term is an entity that requires disambiguation. Without context, “Jaguar” could refer to the animal, a sports team, or the luxury car brand.
2. Check if the Entity Exists Within External Authoritative Knowledge Bases
Determine whether the entity exists in external authoritative knowledge bases such as Wikipedia, Wikidata, or Google’s Knowledge Graph. Ideally, the entity should appear in all three, and you should link to all of them to provide maximum clarity. For industry-specific entities, consider also using specialized glossaries or governing body resources.
For example, the luxury car brand “Jaguar” is defined in all of these databases:
Wikipedia: https://en.wikipedia.org/wiki/Jaguar_Cars
Wikidata: https://www.wikidata.org/wiki/Q30055
Google’s Knowledge Graph: kg:/m/012×34
Therefore, all three should be linked within the entity’s Schema Markup. But how would you link them? This brings us to Step 3!
3. Create JSON-LD Markup and Use Schema.org Properties to Explicitly Define Entities
Implement Schema Markup using Schema.org properties like sameAs, mentions, or about. These properties establish connections between your content and external knowledge bases.
Add JSON-LD markup directly to your website’s code. For example, if your blog post titled “Exploring the all new Jaguar E-Pace” mentions “Jaguar,” the Schema Markup might look like this:
{
"@context": "https://schema.org",
"@type": "BlogPosting",
"mainEntityOfPage": "https://example.com/blog/jaguar–epace",
"headline": "Exploring the all new Jaguar E-Pace",
"mentions": {
"@type": "Brand",
"name": "Jaguar",
"sameAs": "https://en.wikipedia.org/wiki/Jaguar_Cars",
"sameAs": "https://www.wikidata.org/wiki/Q30055",
"sameAs": "kg:/m/012x34"
}
}
4. Test Your Markup
Use tools like the Schema Markup Validator to ensure your JSON-LD markup is correctly implemented. Check for errors or warnings that might affect how search engines interpret your data.
5. Monitor and Update
Regularly review your markup to ensure the links to the external authoritative knowledge bases remain accurate and up-to-date. As the content and entities on your site evolve, you may need to refine, remove, or add new links.
By taking these steps, you can enhance the clarity and authority of your content, ensuring that search engines and users alike understand the precise context of your entities. While seemingly straightforward, entity linking requires consistent maintenance and monitoring, which can be labor-intensive and time-consuming when done at scale.
How We Do Entity Linking at Schema App
At Schema App, we understand the challenges of implementing entity linking manually. Manual approaches often lack scalability, are difficult to maintain, and can lead to outdated or inconsistent data over time. To address these challenges, we developed the Omni Linked Entity Recognition (OmniLER) feature, a dynamic and automated solution for entity linking.
Our OmniLER uses natural language processing (NLP) to identify known entities within your content and link them to authoritative knowledge bases such as Wikipedia, Wikidata, and Google’s Knowledge Graph. By using Omni LER, we can ensure that our customers’ entities are disambiguated and up-to-date, which enables them to develop a robust dynamic content knowledge graph.
If you’re ready to implement entity linking at scale or want to build a comprehensive Content Knowledge Graph for your site, get in touch with us at Schema App to learn how we can help!
Andrea Badder is the Digital Marketing Specialist at Schema App. She specializes in SEO and develops educational resources to help marketing teams understand the value of Schema Markup and Content Knowledge Graphs for semantic search, content strategy, and AI-driven initiatives. Prior to joining Schema App, Andrea worked as a brand strategist and copywriter at a marketing agency. She is also a graduate from the University of Guelph.