Impact of Scaling Entity Linking | Schema App

In the past, we’ve measured the value of Schema Markup purely through the lens of rich results.

However, we’ve seen a lot of changes in rich results and the overall search experience this past year. The uprising of generative AI-powered search engines, accompanied by the volatility in rich results, has prompted our team to dive deeper into the semantic value of Schema Markup and entity linking as it pertains to search today.

In this article, we will share the value of entity linking, the tools enabling you to do it at scale and the results we’ve seen from implementing entity linking with our Enterprise clients.

Growing Importance of Entities in Search

Over the past decade, search engines have shifted from lexical to semantic search to improve the accuracy and relevancy of their search results.

As a result, how we think about search engine optimization also has to change. We have to move away from adding keywords to a page and go towards identifying entities on a page to help search engines and machines understand and contextualize the content on our pages.

Entities are a single, unique, well-defined, distinguishable thing or idea. An entity can be anything from a person to a place to a concept, and they possess defining characteristics or attributes (i.e. colour, price, name). But they need to be described in relation to other things to have meaning. For example, Schema App is an entity that can be described by its name, location, website URL, founders, employees and more.

Your website content contains entities related to your organization, and you can help search engines identify the entities on your page using Schema Markup.

When you implement Schema Markup on your page, you are identifying and describing the entities in your content, which helps search engines better understand your content.

While having entities defined on your site is good, you can go one step further and improve your markup by doing entity linking to build a connected, robust content knowledge graph.

A content knowledge graph is a collection of relationships between the entities defined on your website, defined using a standardized vocabulary like Schema.org. It enables search engines and other machines to gain new knowledge about your organization through inference.

What is Entity Linking?

Entity linking is the act of identifying entities in text and linking them to entities that have been defined in a target knowledge base.

In the context of schema.org, there are two types of entity linking:

• Internal Entity Linking – Linking entities within your own knowledge graph
• External Entity Linking – Linking entities on your site to corresponding entities in external authoritative knowledge bases (i.e. Wikipedia, Wikidata and Google’s Knowledge Graph) for disambiguation

You can use Schema Markup to do both. The Schema.org vocabulary was built with linking entities in mind, by evidence that a property’s expected value is often another type/entity or thing.

Internal Entity Linking

For example, the Product type in the Schema.org vocabulary has the property brand, which can be used to link to another entity typed as Organization or Brand.

If you’ve defined the Product and Organization entities in the Schema Markup on your website, you are building your content knowledge graph. When you nest your Organization entity under the Product entity using the brand property, you’re doing internal entity linking (as long as your entities have unique resource identifiers).

{
  "@context": "http://schema.org/",
  "@type”: "Product",
  "@id": "https://www.example.com/product/blue-shirt/#Product",
  "url": "https://www.example.com/product/blue-shirt/",
  "name": "Blue Shirt",
  "brand": {
      "@type": "Organization",
      "name": "Example.com",
      "@id": "https://www.example.com/#Organization",
      "description": "We offer timeless, high quality mens fashion apparel.",
      "url": "https://www.example.com/",
       }
}

External Entity Linking

In the context of Schema Markup, external entity linking is the act of linking the entities on your site to the corresponding known entities on external authoritative knowledge bases such as Wikipedia, Wikidata and Google’s Knowledge Graph using Schema.org properties. Examples of connector properties include sameAs, mentions, areaServed, and more.

External authoritative databases can differ by vertical or content type. For example, if you are in the medical or finance industry, there may be a governing body or glossary that best defines the entities within your content.

External entity linking can help you define the terms and entities mentioned in your content more explicitly, thus enabling search engines to disambiguate the entity identified on your site with greater confidence and provide users with more accurate and relevant search results.

For example, if your page talks about ‘London,’ this can be confusing to search engines because there are several cities in the world named London. You can help search engines disambiguate which London you are referring to in your content by linking to the same known entity described on Wikipedia, Wikidata or Google’s Knowledge Graph.

Suppose we are talking about the city of London in Ontario, Canada. In that case, we can use the sameAs property to link the entity on your site to the known entity on Wikipedia, Wikidata and Google’s Knowledge Graph. Doing this external entity linking makes it explicit to search engines that the content on the page is about ‘London, Ontario, Canada’ and not ‘London, England’.

  "mentions": {
    "@type”: "Place",
    "name": "London",
    "sameAs": "https://www.wikidata.org/wiki/Q92561",
    "sameAs": "https://en.wikipedia.org/wiki/London,_Ontario",
    "sameAs": "kg:/m/0b1t1",
}

External entity linking is even more vital if your organization is in an industry where being specific is essential (such as defining a medical condition or a specific financial instrument like new construction financing).

Approaches to External Entity Linking

You could take two main approaches to external entity linking: a general approach and a more strategic one.

General Approach to External Entity Linking

You could take a general approach and identify any entity on your site, check if it is a known entity on an external authoritative knowledge base, and, if it is, link that entity to the known entities.

For example, if you are a technology company, your product pages might mention entities like SOC2, Solution, and the United States. Using the general external entity linking approach, you can link these entities to the known entities on external authoritative knowledge bases.

Strategic Approach to External Entity Linking

Alternatively, you can take a more strategic approach and identify a specific type of entity on your site (for example, locations mentioned on your site or a particular term mentioned on your site), check if it is a known entity on an external authoritative knowledge base, and if it is, link that entity to the known entities.

For example, you can use a place-based external entity linking approach to explicitly identify the place entities mentioned on a page and link them to the known entities on Wikipedia, Wikidata and Google’s Knowledge Graph.

If your website has different location-based landing pages for your offering, you can implement place-based external entity linking in your Schema Markup. Doing so would help search engines understand the locations that your organization is servicing and enable your page to perform better on ‘near me’ and other location-based searches.

The entities you target with external entity linking should be purposeful. Instead of linking all the entities on a page with corresponding known entities, you should focus on linking the most essential ones for clarity.

How we do Entity Linking at Schema App

At Schema App, we believe that both internal and external entity linking is crucial to developing a robust content knowledge graph. Both types of entity linking can add value to your SEO efforts and prepare you to get further insights from your content. So, how can you do entity linking within your markup?

You can manually do external linking to the known entities on external authoritative knowledge bases. However, this solution is not dynamic nor scalable, so keeping the data updated and accurate can be resource-intensive and time-consuming.

The Schema App team developed the Omni LER feature to apply entity linking in a scalable, dynamic manner to solve the scale and accuracy of external entity linking.

Omni Linked Entity Recognition (LER) is the automated process of identifying named entities in text and linking them to external identifiers from authoritative knowledge bases (like Wikipedia, Wikidata and the Google Knowledge Graph).

Today, Schema App’s Omni LER feature uses natural language processing to identify entities within a block of text automatically and embed them within the Schema Markup based on the Schema Markup configuration in the Schema App Highlighter.

In the future, we’ll introduce a controlled vocabulary feature to help our customers identify which entities they want to map to for external entity linking. This evolution will give organizations even more control over the topics and entities they want to be known for and how they want to define those entities.

Entity Linking Experiments and Results

The impact of external entity linking on SEO has yet to be explored widely. This prompted our team to experiment with external entity linking to see if it has any measurable impact on SEO metrics.

Using our Omni LER feature, we implemented external entity linking on over 60 enterprise customer accounts in healthcare, finance, B2B technology and other industries.

We ran general and place-based external entity linking experiments on a variety of pages (i.e. blogs, location pages, medical pages, etc.) over three months and measured the impact on search performance. Here’s what we saw as the results.

General Entity Linking Experiment

We took the general external entity linking approach on pages with long-form content, such as blogs. The Omni LER feature within the Schema App Highlighter identified the named entities in the text and embedded the known entities in the markup using the mentions and sameAs properties within the schema markup for the page.

For example, one customer had a blog article about rashes caused by amoxicillin. We used the “mentions” property to identify ‘Amoxicillin’ as an entity on the blog post and further clarified the entity by nesting the equivalent entities defined on Wikipedia and Google’s Knowledge Graph for Amoxicillin.

The Omni LER feature also identified other entities on the page, such as ‘Benadryl’, ‘Keflex’, ‘Mononucleosis’ ‘National Institutes of Health’, and linked these entities to the known entities on Wikipedia, Wikidata and Google’s Knowledge graph under the relevant schema markup property.

After implementing entity linking on that blog article, the customer saw a 336% increase in click-through rate for the query ‘Amoxicillin rash’ and a 390% increase in click-through rate for the query ‘Rash from amoxicillin’. The number of queries for that blog also increased by 86.75%.

Across our customer set, we saw an overall trend where the clicks and click-through rates increased for relevant keywords while the number of irrelevant keywords dropped for each page.

Placed-based External Entity Linking Experiment

In a second experiment, we took the placed-based external entity linking approach on location-based landing pages. This customer had a set of location-based landing pages to cater to their audiences in different states across the US.

We implemented placed-based entity linking on 11 test pages and kept 4 control pages to compare the results.

On the test pages, we added spatialCoverage and audience property in the markup to identify the state this page pertained to (in this example, it was for the state of California) and then further clarified which ‘California’ we were referring to by nesting the equivalent entities defined on Wikipedia, Wikidata and Google’s knowledge graph using the sameAs property.

After running the experiment for 85 days, 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.

Do External Entity Linking at Scale

Based on the early results we’ve seen, external entity linking can help search engines disambiguate the entities mentioned on your site and help your pages show up for more relevant search queries, increasing the clicks and click-through rate to the pages. It is a great way to stand out in search and drive more qualified traffic to your site.

External entity linking can also help your organization build a more descriptive content knowledge graph. You can learn more about content knowledge graphs through our free ‘Content Knowledge Graph Fundamentals’ course.

If you want to implement entity linking at scale or build a content knowledge graph for your site, contact us.