This approach communicates clear relationships, giving machines context about the different entities described on your web pages. By improving search engine understanding of your content, it can lead to better visibility and more accurate presentation in search results.

Here’s a breakdown of nesting in Schema Markup:

Main Entity: This is typically the primary entity or item that your webpage is about. For example, if you’re describing a recipe page, the Recipe itself would be the main entity.

Nested Entities: These are secondary entities on the page that are closely related to the main entity. For instance, if your main entity is a Recipe, nested entities could include AggregateRating and VideoObject.

Image pulled from Google’s General Structured Data Guidelines.

Why is it Important to Nest your Schema Markup?

Nesting your Schema Markup serves several purposes:

1. It clarifies the relationships and hierarchy between different entities defined on your web page.
2. It helps build your content knowledge graph.

Nesting Helps Clarify Hierarchy and Entity Relationships

When implementing Schema Markup, many SEOs often create separate markups for multiple entities on a single page. For example, a page might feature a product along with its color variations and also include reviews and ratings of the product. All this visible content should be marked up with structured data.

However, if you specify each of these entities separately, you miss the opportunity to clearly communicate to search engines the primary focus of your page and the relationships between these entities. Are the reviews and ratings specific to that product, or are they unrelated to the product and pertain to the organization selling it?

This example shows that structuring your entities in a clear hierarchy helps search engines better understand the properties associated with your defined entities and how they all relate to each other.

This leads us to our second purpose for nesting your markup: the development of your knowledge graph.

Building Your Content Knowledge Graph Using Nested Schema Markup

Implementing nested Schema Markup is crucial for building a robust content knowledge graph. A knowledge graph is a collection of relationships between things, aka “entities,” defined using a standardized vocabulary, like Schema.org, from which new knowledge may be gained through inferencing.

Simply put, it is a way to organize your website content into a graph of interconnected entities, enabled through connected Schema Markup.

As search engines advance with AI technologies, establishing a well-defined and interconnected knowledge graph for your organization using Schema Markup is critical to staying ahead. AI search engines can utilize your structured data to uncover new insights about your organization and interpret valuable information from your website’s content and relationships more effectively.

This, in turn, allows your content to show up more accurately and relevantly for user queries.

Additionally, you can use your content knowledge graph to support internal AI initiatives and LLMs like ChatGPT. You can learn more about that in this article.

How do you Nest Schema Markup?

Now that you know what nesting is and why it’s important, it’s time to dive into the “how.” Nesting your Schema Markup can be broken down into several steps.

1. Identify the Main Entity

Determine the primary entity or topic of your web page. The main entity is easier to identify on some pages than others. If you’re unsure which Schema.org type to use, ask yourself what the intent of the page is. Is it selling something? If so, it’s likely a Product or Service. Is it informing an audience about a particular topic? In that case, it’s probably an Article or Blog Posting.

2. Identify Related Entities

Identify the other entities on the page that are associated with the main entity. For example, if your main entity is a Recipe, the webpage containing that recipe may also contain information about the author, reviews and ratings, or even a video showing the steps in the process. Each of these “things” is a related entity that you’ll want to represent in the structured data about that Recipe.

3. Implement a Nested Structure

Use the correct properties to connect your main entity and related entities together. In our Recipe example, we might use the author, review, aggregateRating, and video properties to connect the related entities we identified in the previous step.

Going to the Schema.org page associated with the type of your main entity will help you figure out which properties are available to that type. However, many types have more than 40 properties available to them, so finding the right one can be challenging.

Good news! If you’re unsure which Schema.org properties you should use to connect your entities, you can use our free Schema Paths Tool to identify all possible connections. To use this tool, select the two Schema.org types you want to connect, and it will output all the available properties that connect the two types.

The Schema Paths Tool in the image below shows that you can connect Recipe and VideoObject entities using a number of different properties. In our case, the subjectOf or video properties would both work. But since Recipe is eligible for a rich result, we would also want to consult Google’s Structured Data Documentation to see what they recommend. In this case, VideoObject should be connected to a Recipe using the video property.

Read our article for more information on how to use the Schema Paths Tool.

4. Validate your Schema

The Schema Validator will show all the markup on a single webpage and check for any syntax errors in the code. If the Schema Markup you’ve added aims to achieve a rich result, Google’s Rich Results Test will help you test which rich results your page is eligible for based on the structured data it contains.

5. Maintain Hierarchy

Keep in mind that effective Schema Markup requires ongoing maintenance and management, as on-page content and Google’s structured data requirements are subject to change. This ongoing maintenance will prevent schema drift and ensure you are optimizing your markup for search engine comprehension.

When Shouldn’t You Nest Schema Markup?

If the entities on your page are distinct and unrelated, nesting them within a hierarchical structure may not accurately represent the content. For example, a page featuring both a recipe and a list of unrelated events should use separate Schema Markup for each entity instead of nesting them together.

Additionally, some Schema types and properties are designed to be standalone and should not be nested within other types. For example, BreadcrumbList is used for navigational purposes, while the rest of your Schema Markup is probably intended to represent the meaning (semantics) of the page’s contents. As a result, these markups should not be nested.

Start Nesting Your Schema Markup Today

Implementing proper nested Schema Markup requires expertise and ongoing maintenance. Before worrying about nesting your markup, you must ensure your markup first aligns with your page content and that your entities are properly defined. Although this adds to the challenge, the benefits of this approach can significantly enhance your semantic SEO and provide your organization with agility in response to AI-driven search.

As we rapidly approach a future where AI powers search engines, you can implement nested Schema Markup to help them infer and access new knowledge about your organization. This can improve your visibility to potential customers in search results.

At Schema App, we understand the importance of nested Schema Markup and have designed our solution to help you easily create, connect, and manage your markup.

Don’t let the complexity of Schema Markup hinder your success. Get in touch with us to learn more about our end-to-end Schema Markup solution.

The post What is Nesting in Schema Markup? appeared first on Schema App Solutions.

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 mentioned in text, and linking them to corresponding entities that have been defined in a target knowledge base.

In the context of Schema Markup, 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 knowledge bases 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.

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 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",
}

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 Entity Linking

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

General Approach to 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 entity linking approach, you can link these entities to the known entities on external authoritative knowledge bases.

Strategic Approach to 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 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 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 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 entity linking is crucial to developing a robust content knowledge graph. It 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 link the entities on your page 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 entity linking.

Omni Linked Entity Recognition (LER) is the automated process of identifying entities mentioned in texts and linking them to the corresponding entities on 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 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 entity linking on SEO has yet to be explored widely. This prompted our team to experiment with entity linking to see if it has any measurable impact on SEO metrics.

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

We ran general and place-based 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 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 Entity Linking Experiment

In a second experiment, we took the placed-based 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 Entity Linking at Scale

Based on the early results we’ve seen, 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.

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.

The post Measurable Impact of Scaling Entity Linking for Entity Disambiguation appeared first on Schema App Solutions.

According to Google, an entity refers to a single, unique, well-defined, and distinguishable thing or idea.

Entities can be diverse, ranging from tangible elements like people, organizations, and products to abstract concepts and creative works. They possess defining characteristics or attributes, like size, colour, and duration. And most importantly, entities exist in relation to other things/entities.

Take, for example, “xylopental”. This is a string of characters that have no meaning to humans and, therefore, have no meaning to machines. However, if I invented a new musical instrument named “Xylopental,” this string of letters would become an identifiable entity. It is understood in relation to musical instruments, which is also an entity.

Entities need to be described in relation to other entities to have any meaning.

In its information architecture, Google often refers to entities as “topics.” From a content perspective, we can consider entities in SEO as topics within your content that become well-defined by referencing other related things.

Entities and their connections are crucial in developing Google’s Knowledge Graph. Google’s Knowledge Graph is a database that Google uses to quickly retrieve information about specific topics or entities. Any information Google has on a particular entity will show up in the Knowledge Panel, as shown below.

For example, when we search for “Berkshire Hathaway” on Google, we get a knowledge panel that conveys information about Berkshire Hathaway’s owner, stock prices, revenue, and more.

In the “People also ask” section, we can see queries that don’t specifically name Berkshire Hathaway, like “Does Buffett own McDonald’s?”

As the long-time owner and CEO of Berkshire Hathaway, Warren Buffett is often synonymous with his brand. McDonald’s is Buffett’s favorite breakfast meal, and he had previously purchased 4.3% of McDonald’s stocks but sold it in 1999.

This explains the inclusion of the question “Does Buffett own McDonald’s?” even though it doesn’t mention Berkshire Hathaway at all. All this information is derived through context from entities that are related to one another.

Difference Between Entities and Keywords

A common misconception SEOs have is that entities are just like keywords. Keywords are words or phrases that searchers use in their search queries. It can be a single word, a phrase, a sentence or a question. Historically, search engines would rank pages on the SERP using keyword matching.

However, the method of lexical search presented a few challenges.

1. Keywords tend to be ambiguous because certain words can have multiple meanings. For example, the word ‘Java’ can refer to either the programming language or the island of Indonesia.
2. Different languages tend to phrase the same things differently. For example, the term ‘rebord de fenêtre’ in French translates directly to ‘edge of window’ in English. But it is actually referring to a windowsill.

As a result, the old search algorithms were producing less relevant and accurate results for searchers.

Entities, on the other hand, are universally understood concepts that are not bounded by language or ambiguity. They are broader topics that keywords can stem from. They are distinguishable, especially through their relation to other things. Unlike keywords, entities have an additional layer of context, which can provide greater clarity to search engines.

How do Entities Relate to SEO?

Search engines are evolving toward a more semantic approach, analyzing the concepts and meanings within user queries. They identify relevant pages that answer the entities in question with greater context and accuracy.

As search engines advance in their understanding, there is inevitable demand for SEO strategies to also become more semantic to better align with this sophisticated and nuanced way of search. The good news is that you can assist search engines in grasping the entities and context of the content on your site.

Your website serves as the information hub about things related to your organization. The services provided by your organization, your postal address, your customer reviews, your blog articles – these are all entities related to your organization.

However, the content often exists in the form of plain text, images, videos and infographics. Humans can consume this form of information but machines and search engines cannot comprehend information in this unstructured manner.

Creating Machine-Readable Content

To bridge this gap between human understanding and machine interpretation, implementing semantic Schema Markup to define, describe and connect your entities is crucial. By meticulously defining entities within your content, you are essentially structuring your data in a format that search engines and machines can understand.

You can also further define the entities on your site by linking them to other linked entities in external authoritative databases like Google’s Knowledge Graph, Wikipedia, or Wikidata. This helps search engines disambiguate the entities on your page.

Defining these entities ensures your content is contextually understood by machines. This contextual understanding allows search engines to display your content for a broader range of relevant queries, expanding your site’s visibility and attracting a more qualified audience.

If you leave AI search engines to their own devices without informing them about the entities on your site, you are leaving it to them to decide on what is “true” for your content. You can control how machines interpret your content by defining your entities to prevent hallucinations and inaccuracies from being presented about your organization. This strategic approach safeguards your organization’s E-E-A-T and credibility.

So, now you know why you should define your entities, but how do you do it?

How to Identify and Define Page Entities

Author and Deploy Schema Markup

To have your content topics recognized as entities by search engines, use the Schema.org vocabulary to structure your data. You can use the Schema.org Types and properties to describe the entities across your site.

Many organizations tend to use a Schema Markup plugin to automate their Schema Markup process. However, many of these plugins will only markup certain page Types or properties. As such, you cannot customize your markup to properly define your entities or link them to other entities on your site.

If you want to provide search engines with a clear understanding of your content, you need to describe your entities thoroughly and leverage as many relevant properties as possible. The Schema App Editor and Highlighter are two great options if you want to implement custom semantic Schema Markup on your site.

Add Unique Identifiers to Schema Markup

For your entity to be identifiable and retrievable, it must have a distinct Uniform Resource Identifier (URI). URIs can help machines identify unique resources (like entities) and enable data interlinking.

In JSON-LD, this is expressed with the ‘@id’ attribute. By adding the ‘@id’ attribute to the entities in your Schema Markup, you can easily connect and refer back to other entities on your site so that search engines can clearly understand the relationship between different entities on your site.

For example, the author page for Mark van Berkel contains all the information about the person Mark van Berkel. Therefore, we can use Person markup on that page and define the entity ‘Mark van Berkel’ using the Schema.org properties. When we create the markup, we can add an ‘@id’ so that any connections to Mark can be indicated using the @id.

Search engines like Google can still read and qualify your page for a rich result if you don’t include an @id for your entities. However, you wouldn’t be able to connect the entities on your site in a machine-readable manner.

When you publish your Schema Markup using the Schema App Highlighter or Editor, our tool automatically generates HTTPs URIs for the entities defined in your Schema Markup.

Connect Your Entities

Connecting these entities on your website to explain how they are related, and extending these connections to external knowledge graphs, such as Google’s Knowledge Graph, Wikipedia, or Wikidata, helps search engines to disambiguate the entities on your site.

For example, Mark is one of the founders of the organization Schema App. We can leverage the ‘founder’ property under the Organization type to express that Mark is the founder of Schema App. And since we’ve already defined the entity Mark on his author page, we can link the entity ‘Mark’ using his @id to the entity ‘Schema App’ in the Organization markup.

That way, search engines know that this specific entity, Mark van Berkel, which is described on this page (https://www.schemaapp.com/author/vberkel/#Person), is the founder of Schema App.

As mentioned earlier, you can also connect your entities to external knowledge graphs to distinguish the entities on your site. External knowledge graphs are authoritative databases comprising millions of entities and their relationships. These entities link to other entities across the web which is why they are referred to as “linked entities”.

The linked entities identified in these external knowledge graphs also have unique identifiers, enabling connections to your own entities.

For example, Vancouver is the name of a city in British Columbia, Canada and also the name of a city in Washington State, US.

If your organization is a restaurant based in Vancouver, BC, you can describe your organization’s areaServed property by linking it to the right entity on:

• Google’s knowledge graph (/m/080h2), OR
• Wikipedia (https://en.wikipedia.org/wiki/Vancouver), OR
• Wikidata (https://www.wikidata.org/wiki/Q24639).

That way, search engines can clearly understand which Vancouver you’re referring to.

By establishing these relationships, you empower machines not only to comprehend existing information deeply but also to infer new knowledge based on this contextual understanding.

How do Entities Relate to Knowledge Graphs?

This process of defining and connecting entities effectively constructs a robust knowledge graph for your organization, providing a comprehensive and accurate representation of your content from a digital scope. Entities serve as the foundational building blocks of information that knowledge graphs organize into explicit relationships.

By capturing these complex relationships between entities and building context, knowledge graphs provide machines with a robust understanding of how different entities are related. Linking your entities internally and externally enriches the information available to search engines to create a holistic view of your organization.

This approach also helps prevent misrepresentation of your content and avoids machine confusion between ambiguous entities. Consider the thing, “Apple”, as an example; it could refer to the fruit or the brand. By linking your entity to the relevant external definition using the sameAs property, you offer an explicit distinction and enable search engines to align your content accurately with user queries.

Schema App Helps Define Your Entities & Develop Your Knowledge Graph

You can help search engines further understand, contextualize and distinguish the entities on your site using Schema Markup. If you are looking to leverage semantic Schema Markup to define your entities and develop a robust marketing knowledge graph for your organization, we can help.

At Schema App, we help enterprise SEO teams leverage semantic Schema Markup to define and link their entities, develop their knowledge graph, and improve search performance. Visit our website to learn more about our Schema Markup and knowledge graph solution.

Curious about how we can support your organization? Fill out this form to get started and connect with us.

The post What is an Entity in SEO? appeared first on Schema App Solutions.

In this article, we’ll explore what Linked Data is and share examples of Linked Data projects which many in the SEO and tech community refer to as knowledge graphs.

What is Linked Data? 

Linked Data is a set of design principles for publishing machine-readable interconnected data on the web. 

The term “Linked Data” first appeared in 2006 when Tim Berners-Lee published a design note about the Semantic Web. He sought to use Linked Data as a way of representing the relationship between different things on the internet. The internet would then become a huge database of interconnected (linked) objects (data) and become the Semantic Web.

Businesses or enterprises can use Linked Data to define things and the relationships between them. For example, companies like Facebook, Twitter, and LinkedIn have undertaken Linked Data projects to represent social networks. When users perform actions like connecting to other users or liking and resharing content, these actions are reflected in a graphical representation of who they are, who they know and what they like.

As a result, these social media platforms can gain knowledge about a person and enable things like targeted advertising to users based on their relationship to other things. However, the knowledge that these social media platforms derive from their Linked Data is proprietary and not licensed for external use.

This led to a movement calling for Linked Data to be open for people to use freely for research purposes – especially from governmental organizations, and other public institutions such as museums.

In 2010, Tim Berners-Lee modified his original design note to add principles for Linked Open Data, a variation of Linked Data permitting reuse.

Linked Open Data (LOD) is Linked Data released under an open license, which allows others to freely access and reuse it.

Principles of Linked Data

When Tim Berners-Lee first published the design note about Linked Data, he defined the 4 principles of Linked Data. Based on this note, people or machines would be able to explore the web of data if it adhered to the following 4 principles:

1. Use URIs as names for things

A URI (Uniform Resource Identifier) is a string of characters that identifies a resource. It provides a consistent way to identify resources across different systems and protocols.  A resource (also known as an entity) is anything that can be identified and described, such as people, places, objects, or concepts.

2. Use HTTP [or HTTPS] URIs so that people can look up those names

An HTTP/HTTPS URI is a specific type of URI that uses the Hypertext Transfer Protocol or Protocol Secure. This means that when you access an HTTP/HTTPS URI in a web browser or through an HTTP request, you should get back information about the resource it identifies.

3. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL)

When a user or application accesses a Uniform Resource Identifier (URI), the information returned should be both meaningful and structured according to standardized semantic technologies, specifically RDF (for expressing the data) and SPARQL (for querying the data).

4. Include links to other URIs so that they can discover more things

When you create or publish data, you should include links within your data to other resources or entities (in the form of URIs). These URIs can point to related or relevant information, such as other resources, definitions, or attributes. This provides additional context about your own resources.

By following these principles, you contribute to a growing network of interlinked data across the web. This allows consumers of the data (human or machine) to gather more insights, context, and knowledge.

The benefit of using Linked Data for SEO

Linked Data is great for SEO because it can provide search engines with more contextual knowledge about your content. Search engines now look at relevancy to provide searchers with the most accurate results.

One of the most common forms of linked data on the web is Schema Markup which primarily describes webpage contents for search engines. Schema Markup uses the Schema.org vocabulary which can express RDF linked data in formats like JSON-LD.

When you use machine-readable code like Schema Markup to express the relationship between the entities on your site, it helps search engines understand and derive knowledge about your organization.

For example, if you have a page about your organization’s proprietary software application, you can tell search engines that this software application is provided by your organization by linking the URI that contains all the information about your organization to the provider property in the markup for your page.

{
  "@context": "http://schema.org/",
  "@type": "SoftwareApplication",
  "@id": "https://www.schemaapp.com/solutions/schema-app-highlighter/#SoftwareApplication",
  "name": "Schema App Highlighter",
  "description": "Use the Schema App Highlighter to customize your Schema Markup...",
  "applicationCategory": "Search Engine Optimization",
  "provider": {
    "@type": "Organization",
    "@id": "https://www.schemaapp.com/#Organization",
    "url": "https://www.schemaapp.com/",
    "name": "Schema App",
    "description": "Schema App is an end-to-end Schema Markup solution...",
    "telephone": "18554448624",
    "email": "support@schemaapp.com",
    "areaServed": "http://www.wikidata.org/entity/Q13780930",
  }
}

The URI appears in JSON-LD in the @id attribute. Your Schema Markup can be generated and authored without including identifiers (@id). Search engines like Google will still read it and make it eligible for rich results. However, by generating your Schema Markup with a URI, you can link it to other entities.

You can also link your Schema Markup to other Linked Data projects to be more explicit about the entities you are talking about on your website.

For example, if you are talking about football on a page, this can be confusing to search engines because the term football can mean different things depending on where you are in the world. You can help search engines disambiguate which football you are referring to by linking your page to the same entity described in Wikipedia, Wikidata or Google’s Knowledge Graph.

If you are talking about American football, you can use the sameAs property in your Schema Markup to link to the same entity on Wikipedia, Wikidata or Google’s Knowledge graph.

{
  "@context": "http://schema.org/",
  "@type": "BlogPosting",
  "@id": "https://www.schemaapp.com/blog/what-is-football/#BlogPosting",
  "url": "https://www.schemaapp.com/blog/what-is-football/",
  "name": "What is Football?",
  "headline": "What is Football?",
  "description": "Learn about the rules and history of Football.",
  "mentions": {
    "@type": "Thing",
    "name": "Football",
    "sameAs": "https://www.wikidata.org/wiki/Q41323",
    "sameAs": "https://en.wikipedia.org/wiki/American_football",
    "sameAs": "kg:/m/0jm_",
  }
}

However, applying Linked Data on your site can be a technically challenging task.

1. Quality – You need to keep the data up-to-date, accurate and complete.
2. Scalability – Handling this huge volume of data can be time-consuming and resource-intensive.
3. Expertise – Transforming your content into Linked Data requires knowledge of the technologies to do this work and how to apply them effectively.
4. Sustainability – You need resources to maintain the data quality.

Examples of Linked Data Projects in SEO

There are many examples of Linked Data Projects in use today. These Linked Data Projects are also often referred to as Knowledge Graphs.

Knowledge graphs are a collection of related entities expressed as RDF triples. When you use Schema Markup to express the relationship between two entities on your site, you are implementing Linked Data. When you connect the various entities on your site, you are effectively developing an internal knowledge graph about your organization. Your internal knowledge graph becomes even more robust and useful when linked to other external knowledge graphs.

Some of these external knowledge graphs are also useful for search engine optimization (SEO) purposes. SEO teams can connect their internal knowledge graphs to external knowledge graphs to tell search engines that the entity defined in this external knowledge graph is the same as the entity defined on their website.

Let’s explore some of the Linked Data Projects / External Knowledge Graphs pertaining to the SEO world.

Google’s Knowledge Graph

Google’s Knowledge Graph is a knowledge database that Google uses to provide quick answers to queries about certain topics or entities (people, places, organizations, things). This can show up in search in the form of a knowledge panel. The knowledge panel contains a snapshot of information about the topic based on Google’s understanding of the available content on the internet.

The story of Google’s Knowledge Graph starts with Freebase, a Metaweb project launched in 2007. Freebase was described as “a system for building the synapses for the global brain”. This massive knowledge base, which formally became a linked open data project in 2008, was one of the largest and most ambitious Linked Data projects of its time.

In 2010, Google acquired Freebase from Metaweb and imported Freebase’s massive knowledge base into Google’s proprietary Knowledge Graph. Soon after, Google introduced their Knowledge Graph in their famous ‘things, not strings’ article, indicating a pivot from lexical to semantic search.

The Google Knowledge Graph is a Linked Data project because it adheres to the 4 principles of Linked Data. However, the Google Knowledge Graph is NOT a linked open data project because the data is not published with an open license. That being said, it is possible to find identifiers (URIs) for entities in the Google Knowledge Graph and link them to your own knowledge graph.

How to access Google’s Knowledge Graph?

The Google Knowledge Graph has a search API that is read-only. You’ll notice the URIs in the output are structured with a “kg” namespace (which stands in for http://g.co/kg) and either /m/ or /g/ before a string of characters. These identifiers are called “mid”s, or Machine IDs, which is a legacy term from Freebase.

For example, the Freebase object for the entity Barack Obama has the mid /m/02mjmr. This same entity can be accessed in Google’s Knowledge Graph by going to https://www.google.com/search?kgmid=/m/02mjmr. The entity has the same mid in Google’s Knowledge Graph.

How is Google’s Knowledge Graph used?

Google uses its knowledge graph to improve the search experience on its search engine. When you search for something like “Berkshire Hathaway”, Google identifies the entities in your query and provides information on those entities from both its knowledge graph and other sources on the web. One of the most common sources is Wikipedia.

Wikipedia & DBpedia

Wikipedia is a free, collaborative online encyclopedia composed of more than 61 million articles. Wikipedia articles represent entities, such as people, places, events, concepts, or other things.

The URLs of Wikipedia articles also function as URIs for the entities they represent. So the URL https://en.wikipedia.org/wiki/Kathryn_Janeway is both an article that can be visited on the web, and the URI that represents the entity, Kathryn Janeway, in Wikipedia’s knowledge base.

Articles within Wikipedia contain structured elements, as well as links to other related entities. While Wikipedia on its own isn’t a traditional Linked Data project, it plays a significant role in the Linked Data ecosystem on the web, particularly with regard to DBpedia and Wikidata.

DBpedia is a linked open data project that extracts information from Wikipedia to generate RDF triples, which can be semantically queried alongside other related datasets. It pulls information from the structured elements of Wikipedia pages, such as “infobox” tables like this:

While Wikipedia might be an excellent source of summarized information for general use, the depth and breadth of information on Wikipedia means it has become an essential source of training data for many AI initiatives such as natural language processing, named entity recognition, and the development of Knowledge Graphs like Google’s Knowledge Graph.

Post from Wikipedia Engineering Manager, Joseph S.; inspired by https://xkcd.com/2347/

Wikidata

Wikidata is a collaborative Linked Open Data project that’s been operated by the Wikimedia Foundation since its inception in 2012 (source).

Despite having Wiki in its name, Wikidata is not the same as Wikipedia. Wikidata is a broader knowledge base than Wikipedia, containing data about a wider range of topics. Wikidata also allows users to create RDF Linked Data directly.

Even though Wikidata and DBpedia are both Linked Open Data projects related to Wikipedia, they have different aims and serve different functions.

DBpedia extracts information to generate Linked Data from Wikipedia’s structured sources like infoboxes. As a result, DBpedia treats the knowledge derived as facts.

Rather than extracting information from Wikipedia, Wikidata creates Linked Data for Wikipedia (source). Since Wikidata also treats statements within the Linked Data as claims rather than facts, these statements must be annotated with provenance information (i.e. who made each claim).

Instead of “mid”s (identifiers used by Freebase/Google’s Knowledge Graph), each entity in Wikidata has a “qid”.

Here’s a summary of the identifiers for each of the Linked Data projects listed above.

Google’s Knowledge Graph, Wikipedia, and Wikidata are the most common Linked Data projects utilized in SEO. When we talk about connecting your Schema Markup to external authoritative knowledge bases at Schema App, these are the external knowledge graphs we are referring to.

How to use Linked Data with Schema App

At Schema App, our semantic technologies allow SEO teams to easily generate Linked Data for their website content.

Generate URIs for your entities

When you publish your Schema Markup using the Schema App Editor or Highlighter, our tool automatically generates HTTPS URIs for the entities you define in your Schema Markup. These URIs, which appear in the @id attribute, link to the URLs of the pages where they’ve been mentioned.

For example, we publish Organization markup to our Schema App home page. The URI for our Organization entity would then be the URL of our homepage + #Organization – https://www.schemaapp.com/#Organization. If you navigate to this URI, it will take you to the page about our Organization.

Creating URIs for entities on your site allows you to easily link to those entities in your Schema Markup. For example, if your organization has published a blog post you can link your Organization URI to the publisher property in your BlogPosting Schema Markup.

{
  "@context": "http://schema.org/",
  "@type": "BlogPosting",
  "@id": "https://www.schemaapp.com/schema-markup/what-is-a-rich-result/#BlogPosting",
  "url": "https://www.schemaapp.com/schema-markup/what-is-a-rich-result/",
  "name": "What is a Rich Result?",
  "headline": "What is a Rich Result?",
  "description": "A rich result is an enhanced search result shown on search engine results page. Find out how you can achieve a rich result for your page.",
  "publisher": {
    "@type": "Organization",
    "@id": "https://www.schemaapp.com/#Organization",
    "url": "https://www.schemaapp.com/",
    "name": "Schema App",
    "description": "Schema App is an end-to-end Schema Markup solution...", 
    "telephone": "18554448624",
    "email": "support@schemaapp.com",
    "areaServed": "http://www.wikidata.org/entity/Q13780930",
  }
}

Linking to external entities

Our tools also allow SEO teams to link to external entities using a variety of methods such as:

• Manually linking to external entities using properties like sameAs and additionalType
• Automatically linking to external entities using our Omni Linked Entity Recognition feature in the Schema App Highlighter tool
• Automatically linking the categories and tags on a WordPress site to existing Wikipedia and Wikidata entities using our Advanced WordPress plugin.

You can read this article to learn more about our entity linking methods.

Overcome the challenges of implementing Linked Data

In summary, Linked Data facilitates the connection of data from different sources to provide machines with more contextual information, enabling them to infer new knowledge from existing facts.

Applying Linked Data through the Schema Markup on your site can help search engines understand the relationship between the entities on your site and disambiguate the entities mentioned in your content.

If you need help implementing Linked Data on your site, we can help. At Schema App, we provide SEO teams with the tools and expertise to implement Linked Data at scale. Get in touch with us to learn more.

The post Linked Data in SEO: What You Need To Know appeared first on Schema App Solutions.

Fast forward to today, search engines now prioritize positive user experience and ‘people-first’ content. Search engines consider content depth, meaning (aka semantics), and how it answers user questions by providing the desired information.

Businesses must adapt to this evolution of search. As search engines become more sophisticated, incorporating semantic understanding into your search engine optimization (SEO) strategy is crucial to keep up with the changing landscape. This will help ensure your content remains relevant and visible to your target audience.

Understanding Semantic SEO

The word ‘semantic’ is all about understanding the meaning of language.

When people use the term ‘arguing about semantics’, they’re usually debating the interpretation (or misunderstanding) of words or phrases. Semantics is a field that examines how language conveys meaning and follows certain rules for effective communication.

Semantic SEO is the process of giving more meaning and context to your web content to help search engines gain a better understanding of your content.

Why is Semantic SEO important?

The way that search engines understand your content has changed

Historically, Google solely used keywords to evaluate a web page’s topic and relevance to a search query. As of Google’s algorithm changes made in 2013, however, instead of only looking at keywords to understand what the page is about, search engines now read and understand a page’s overall topic.

This change allowed search engines to provide users with a better search experience and ensure that the results presented are providing users with the answers they are looking for.

To improve your ranking and web traffic

By utilizing semantic SEO, search engines can better understand your content and more accurately relate it to search queries. In return, your pages can rank higher on relevant searches, leading to more impressions and, ideally, more clicks. 

Because it presents users with the most relevant information based on their queries, those who do visit your pages are more easily converted into customers. This is because it’s more likely to be exactly the information/product/service they were seeking. 

To keep up with generative AI search

Semantic SEO is the future of search, and that future has already begun. The emergence of powerful generative AI search engines like Google’s Search Generative Experience, has propelled semantic technology to unprecedented heights.

In this transformative era with the AI revolution and search generative experience, search engines are gaining an unprecedented ability to interpret the nuances and meaning of human language. As a result, search queries are now returning dynamic and tailored results with the potential for conversational follow-up answers.

While traditional SEO practices, including keyword research, remain valuable in digital marketing, integrating semantic technologies like Schema Markup into your strategy can provide a competitive advantage.

By doing so, your pages become more visible and comprehensible to the intelligent systems that bridge the gap between your content and human users.

How is Semantic SEO Different From Traditional SEO?

Where traditional SEO prioritizes content that is keyword-based, semantic SEO is a topic-based approach that increases the likelihood of connecting users to information that is most relevant to their search query. 

It accomplishes this by focusing on both the meaning behind queries and the contextual information and relationships in the content being retrieved. This results in a better user experience which can lead to a lower bounce rate, as those who end up on your page from search have a higher intent to consume the information presented.

Semantic SEO is the bridge between your content and users’ intent. This is the biggest difference between Traditional SEO and Semantic SEO. – WeDevs

Moving from a keyword-based to a topic-based approach with your content can seem a bit abstract at first. After all, it’s simple enough to do some keyword research, find a list of terms, and then write content to string the terms together.

These same skills are still essential when it comes to semantic SEO, with one key difference: entities.

What are Entities?

To put it plainly: entities are things, and things have dimensions! 

They take up space (be it physical, digital, or conceptual). They also have attributes (like colour, size, duration) and, most importantly, they are understood in relation to other things.

Take, for example, “bestgihrtie”. This is a string of characters and it means nothing to a human brain, so it won’t mean anything to a search engine either. But if I decide it’s the name of my new album, snackfood, or generative AI tool, this jumble of letters now becomes an identifiable entity. In other words, the string becomes a thing.

However, that entity needs to be described for it to have any meaning. “ChatGPT” didn’t mean anything until we started hearing about it in relation to generative AI, chatbots, and productivity. 

This same entity took on a different meaning when we heard about it in relation to hallucinations, misinformation, algorithmic bias, and plagiarism. The word “relation” is doing the heavy lifting in this example since what it’s providing is context. 

We as humans use context clues to make sense of new things and search engines are doing the same thing.

That being said, machines, including search engines, aren’t good at understanding in the same way that human brains can. Search engines use natural language processing (NLP) to analyze the proximity and frequency of certain terms, phrases and entities.

There are ways, however, to make statements about entities more explicit for search engines.

Elevating Search with Entities

As previously stated, semantic search goes beyond traditional keyword matches and focuses on delivering topically relevant search results.

Instead of simply providing “plain blue links” to web pages, it can present information in various formats, such as Knowledge Panels, Featured Snippets, and Rich Results, all centered around the primary entity being searched.

This approach aims to provide users with more comprehensive and contextually relevant information related to their search query. Let’s look at an example of how a search for “Gibson Les Paul” yields results about this particular entity.

Under the “People also ask” section, we can see queries that don’t blatantly name the type of guitar, like: “How much did Kirk Hammet pay for Greeny?”. 

Greeny is a 1959 Gibson Les Paul Standard, named after its original owner, Peter Green. It happened to be purchased by Kirk Hammet, the guitarist of Metallica, which also explains the inclusion of the question “Who is the richest member of Metallica?”, which has nothing to do with guitars at all.

But if we think about this information as being derived from entities that are related to one another, the inclusion of these “People also ask” queries make sense.

And if we search for “Greeny guitar”, we’ll get a Knowledge Panel conveying some of the attributes of this particular guitar, including the fact that its manufacturer is “Gibson”.

Leverage Schema Markup to Improve Your Semantic SEO

There are many things you can do to implement semantic SEO. A lot of it involves creating clusters of content surrounding the topic that you want to be known for.

However, in addition to this, you need to ensure search engines understand what your content is about and how the entities in your content are connected. Implementing Schema Markup allows you to categorize entities and explicitly relate them to each other, providing search engines with helpful contextual information about your content.

Schema Markup, also known as structured data, is a standardized vocabulary that search engines analyze to understand the content on your web pages. By implementing Schema Markup through code, such as JSON-LD, search engines can contextualize your content and present it to users searching for relevant and related topics.

While machines don’t interpret information like humans do, Schema Markup helps bridge the gap. It does this by providing explicit details about the content on your pages, ensuring search engines accurately comprehend the topics of information your website offers.

One of the most common uses of structured data is the application of the Schema.org vocabulary expressed in JSON-LD. It’s usually found under the “technical SEO” umbrella, and most would know it as the “Thing” responsible for rich results.

Rich results can drive higher click-through rates with their engaging visuals, but if that’s the extent of your Schema Markup application, your semantic SEO strategy is missing out!

So how can you leverage Schema Markup to improve your semantic SEO?

1. Implement more specific Schema Markup to clearly explain what your page is about

To be semantic, search engines need to clearly understand your content.

Content publishers often use generic Schema Markup plugins to add default Schema Markup on certain pages like blog articles, product pages, home page, etc. However, the downside of doing this is the lack of control over your Schema Markup.

Generic Article markup autogenerated by plugins won’t give your content the richly descriptive Schema Markup that best supports the search engines.

Plugins are usually CMS-specific and tend to map more general properties to available metadata (like author, or datePublished). While these properties are still helpful, they don’t describe the content with as much depth as more specific properties like about or mentions, which can be used to call out topics and entities in an Article.

Your markup will also often be disconnected. Each page may have Schema Markup describing the content, but not necessarily how that content relates to other pages across your website.

2. Add @ids in your Schema Markup

Your Schema Markup can be generated and authored without including identifiers (@id). Search engines like Google will still read it and make it eligible for rich results.

In the JSON-LD syntax, @id is used to provide URIs (uniform resource identifiers) to entities in your Schema Markup. These identifiers allow you to refer back to entities as you build your knowledge graph.

In the example below, the Organization entity created for Schema App’s homepage has the @id “https://www.schemaapp.com/#Organization”. If a blog post on another page wants to say that it was published by the Organization Schema App, the Schema Markup for that page would say the publisher is “https://www.schemaapp.com/#Organization”.

@ids give the entities in your markup unique identifiers.

Think of it like your social insurance number! There may be 10 different people named “Jane Doe” in your organization, but each of them will have a unique ID that differentiates them. Schema App auto-generates @ids for every entity, so you can link the unique entities across your website.

Therefore, if you want to improve your semantic SEO, you should add @ids to your JSON-LD Schema Markup.

3. Connect your Schema Markup to develop your knowledge graph

Establishing a connection between your Schema Markup elements is crucial for developing a comprehensive knowledge graph. Knowledge graphs are necessary for describing how things on your site are related to each other, as well as other things on the internet.

It makes your content more semantic and provides search engines with contextual knowledge about your content.

Connect Your Entities On Your Website

On your website, you can connect different entities to one another. For instance, if you have a law firm with multiple service pages, it’s important to connect those service pages to your organization. This indicates that your organization provides all of those services despite them being on separate pages.

To ensure accurate representation, it’s vital to describe the relationships between marked-up entities in detail. For example, you need to clarify if an article is about a specific topic or if it simply mentions it.

Schema App offers a free Schema Path tool that helps identify available properties to connect your entities effectively.

Connect Entities to External Authoritative Knowledge Bases

You can also connect entities on your site to external authoritative knowledge bases such as Wikidata or Wikipedia. By doing so, you are clearly explaining what your entity is about.

For example, let’s say your page talks about football. Football can mean two different sports to different readers. In America, football is American football while in Europe, football is soccer.

So if your page is about American football, you can link it to the Wikidata entity (https://www.wikidata.org/wiki/Q41323) for American football in your Schema Markup using the sameAs property. This will help search engines understand that your page is referring to American football and reduces the risk of misinterpretation.

By connecting entities on your site to other entities and external knowledge bases, you are forming your own knowledge graph. The @ids that we mentioned earlier clearly identify the entities in your content, allowing you to connect them and build context.

With Schema App, you have the flexibility to add these entities either manually through our Editor or automatically through the Highlighter, utilizing the Linked Entity Recognition feature. For WordPress users, our WordPress plugin can automatically identify and link entities that you have included in your tags and categories.

Download our Guide to Connected Schema Markup to learn how to connect the entities on your site and build your knowledge graph. 

The Future is Semantic

When creating website content for SEO, it’s important to prioritize semantic SEO that focuses on topics rather than just keywords. Search engines now understand context, relationships, and user intent better than ever before.

To stay competitive on SERPs, you need to create relevant, high-quality content that targets specific topics and use connected Schema Markup to help search engines understand how your content relates to user intent, search queries, and other information on the internet.

By embracing semantic SEO, you align your strategy with search engines’ evolving understanding. This leads to better visibility on the SERP and the delivery of highly-tailored content to your target audience.

If you’re looking to implement connected Schema Markup at scale for your site, get in touch with our team to learn about our solution.

The post Semantic SEO: What You Need to Know appeared first on Schema App Solutions.

This January, we’ve seen December’s fluctuations turn into a steady rhythm of growth. There were also discussions about Google’s perspective on connected structured data. Here’s a roundup of what we’ve seen this past January.

Rich Results Performance in January 2023

Steady performance across all rich results

January was a fairly calm month on the search engine results page – a welcomed change from the second half of 2022. 

According to our data from Schema Performance Analytics, most rich results were trending positively in January in comparison to December.

FAQ Recovery from December Update

In December 2022, we saw the performance for FAQ rich results take a slight decline.

This is likely due to the rollout of Google’s December Helpful Content Update and link spam update, which affected rankings and traffic to many websites as observed by other SEOs. 

The Google search ranking algorithm volatility is still pretty hot, right before Christmas https://t.co/plitzoaipq pic.twitter.com/HWgLMFlxhI

— Barry Schwartz (@rustybrick) December 23, 2022

Thankfully, the updates ended officially in January and we’ve seen the numbers recover and stabilize for the rest of the month. 

Connected vs. Unconnected Structured Data

This January, there was a conversation with John Mueller about whether Google had an SEO preference for connected structured data on Mastodon. 

While John said that Google has no preference, Ryan Levering, who primarily works on structured data at Google, agreed to John’s response but with a caveat on Mastodon. 

Here’s our takeaway from this discussion. 

Google cares about Schema Drift.

In Levering’s response, he said,

However, the caveat here is that when you do [structured data] in multiple blocks, there are sometimes conflict/duplication problems… We still see cases, where people throw unrelated markup about things (like related products) at the same top level as the main entity from different blocks on the page and that, makes it mostly noise.”

The duplication issue is also known as Schema Drift and happens when the structured data is out of sync with the content on your website. 

During the “Structured Data: What’s it all about?” episode of Google’s Search off the Record Podcast, Levering mentioned that their primary challenge with structured data is “figur[ing] out a way to verify that the structured data is accurate”. 

The challenge of verifying the accuracy of structured data is costly to Google, “so sometimes centralizing the logic makes it more consistent/correct,” said Levering. Keeping the structured data organized can reduce the risk of Schema Drift and this aligns with how we do structured data at Schema App. 

The Schema App Highlighter dynamically generates structured data from the content on your website. If your team changes the content on the page, your structured data will automatically update to reflect those changes. 

Connected structured data might not matter as much to Google today but it will in the future.

Also, over time richer/correct semantics will favour more connected graphs.” 

During the podcast, Levering also spoke about how machine learning can use structured data as a data source. If machine learning uses structured data to validate or understand the content, connected structured data will teach the machine to infer the meaning and connections between different entities on the site. 

Even though Google doesn’t utilize connected structured data today,  they are likely headed in that direction in the future. However, creating connected structured data can be complicated and hard to manage. 

At Schema App, we do more than just achieve rich results through structured data. We help our customers create their own knowledge graph and connect it with other knowledge graphs available on the web. 

With our background in semantic technology, our goal is to support our users’ SEO goals while also connecting their domains with the people, places, things and concepts that have been described by other authorities like Wikipedia, Wikidata, and Google’s Knowledge Graph. In engaging with Schema App, you are leveraging a global collective intelligence and transforming your data into knowledge.

Connected Schema Markup is essential to this work. That’s why we developed the Schema Paths Tool, to help users connect different schema types and determine the path that best articulates the relationships in their web content.

Conclusion

Rich results are merely the tip of the structured data iceberg. 

At its most basic level, structured data increases search engines’ understanding of your content, which corresponds to an increase in traffic and engagement to your site. At an advanced level, structured data provides search engines with context to the contents on your page, which transforms data into knowledge for future applications. 

If you are interested in generating connected structured data, we can help. Get in touch with us today to learn more about our solutions.

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