With user behaviors and search engine algorithms constantly changing, it is crucial to ensure that your new and old content remains comprehensive, relevant, and helpful to humans and machines.

Enter Schema.org, a collaborative, community-driven initiative launched in 2011 by tech giants Google, Bing, Yahoo, and Yandex. The Schema.org vocabulary provides a standardized framework for structuring and organizing data on the web. It offers a comprehensive set of Types and properties that website owners can use to describe the entities and content on their site.

While Schema.org is widely known for helping search engines and machines understand the content on your site, its potential extends far beyond that. This vocabulary can be a game-changing checklist for improving your website’s content and formatting and identifying information gaps on your pages. By aligning your content with relevant Schema.org Types and properties, you can identify opportunities you may have overlooked, enhancing your overall content strategy.

In this article, we’ll explore how you can leverage the Schema.org vocabulary to develop a thorough and robust content strategy for your website.

Identifying Content Gaps on Your Website

Your primary focus should always be creating high-value content that serves your users’ needs. That said, the Schema.org vocabulary can serve as a roadmap during the content creation process, outlining information commonly found on certain types of web pages.

The Schema.org vocabulary provides a detailed framework for describing the entities on your website and their relationships. By examining the properties associated with relevant Schema Types, you can:

• Identify potential gaps in your existing content
• Fill those information gaps for your audience
• Add depth to existing page content
• Create new, supplementary content

Whether you’re creating new content or revamping existing pages, Schema.org can provide valuable guidance. Let’s explore a few examples.

For a healthcare organization creating a new page about a medical condition, you’ll need to decide what information you should include. The MedicalCondition Type in Schema.org has a list of properties such as signOrSymptom, possibleTreatment, and more that capture information that is commonly found on these types of pages. Reviewing the full list of properties associated with a Type can spark ideas about what entities are well-suited for supplementing your content.

Recall that you can only mark up content that exists on your page. Therefore, if you want to incorporate content for these properties to give readers more comprehensive information about the subject, you’ll want to identify those opportunities early in the content creation process.

Our customer, Sharp Healthcare, successfully applied the Schema.org vocabulary to form a long-term content strategy. During their website migration process, the Sharp team ensured that each page focused clearly on a specific Schema.org Type when establishing their content structure. They also incorporated content for the properties suggested by Schema.org for each chosen Type.

Enhancing Rich Results Potential

Aligning your content with Schema.org properties not only helps fill content gaps but also improves your rich result eligibility on Google’s search engine results pages (SERPs). Rich results can lead to enhanced visibility on the SERP and increased click-through rates for your website.

To be eligible for rich results, specific content elements must often be present on your pages. Each rich result type typically has both required and recommended properties:

1. Required properties: These are essential for eligibility and must be on your page.
2. Recommended properties: While not mandatory, Google has stated that including more recommended properties can improve the quality of rich results for users and that rich result ranking takes extra information into consideration.

By incorporating both required and recommended properties into your content strategy, you can simultaneously improve your content quality and rich result eligibility.

Let’s look at product snippet requirements and recommendations as an example:

Product Snippet
To be eligible for product snippets on the SERP, it is required that you include the following:

• name (of the product)
• At least one of the following is required, but all are recommended:
  • review
  • aggregateRating
  • offers

You can further enrich your product rich result by adding content around the pros and cons of your product. That way, you can markup the content using the positiveNotes and/or negativeNotes properties and potentially have these pros and cons show up on your product rich results.

CAPREIT successfully leveraged Schema.org to enhance their rich result potential. By structuring their content according to Schema.org guidelines, they were able to improve their visibility in search results for their property listings and job postings.

Continuous Content Optimization

While Schema.org is an excellent tool for identifying content gaps and structuring information, it’s crucial to remember that creating helpful, high-quality content should always be your primary goal.

You must have substantive, relevant content in place before implementing Schema Markup. Without this foundation, you won’t be eligible for rich results. Moreover, attempting to markup non-existent or irrelevant content could be seen as spammy, potentially leading to penalties from Google.

Use Schema.org as a starting point to spark ideas and ensure your content is comprehensive, but don’t let it constrain your creativity or limit the value you provide to your audience.

By balancing user-focused content creation with Schema.org’s structured guidance, you can develop a content strategy that provides genuine value to your users and enriches the Schema Markup on your site.

Schema App Provides Content Recommendations Using the Schema.org Vocabulary

The digital landscape is constantly changing, and with it, Google’s structured data documentation and the Schema.org vocabulary continue to evolve. By keeping up with the latest updates, you can continually refine your content strategy and ensure your website content remains aligned with best practices.

At Schema App, we help our customers stay current with the latest changes in Schema.org and Google’s documentation. We also provide content recommendations to help our customers improve their rich result eligibility and enhance the richness of their content.

The post How to Improve Website Content Using the Schema.org Vocabulary appeared first on Schema App Solutions.

In the digital marketplace, incorporating robust Schema Markup is one of the most critical steps you can take to get noticed. Schema Markup enables search engines to understand your web page content, effectively rank it, and present users with relevant search results.

What Is Schema Markup?

Schema Markup, also known as Structured Data, is data that you can add to your web page’s HTML code to explicitly define entities, properties, and relationships within your content. By doing so, it assists search engines in better comprehending and contextualizing your page content, thereby enabling them to deliver more accurate search results to users.

Schema Markup is created using the Schema.org vocabulary, a collaborative project involving major search engines like Google, Bing, Yahoo, and Yandex. The primary objective of Schema.org is to establish a standardized vocabulary for describing content on web pages, making it simpler for search engines to understand and interpret the meaning of various elements present on a webpage.

Although search engines use sophisticated machine learning algorithms, machines do not process or interpret information in the same way as humans. What might seem simple to a person may be unintelligible to a computer. Schema Markup helps fill in the blanks for search engines so that they know exactly what your page is about.

For instance, let’s say you have a product detail page containing an image and description of a handsaw, along with an image of the brand in the header.

A person reading this would immediately realize that the handsaw is from the brand “Dewalt,” but it might be difficult for search engines to understand that explicitly. You can use Schema Markup to identify that the brand of handsaw on this page is “Dewalt” so that the search engine can present this content for individuals searching phrases like “Dewalt handsaw.”

What are the main SEO benefits of using Schema Markup?

1. Help search engines understand the content on your page

Schema Markup enriches your website content by organizing its data in a way that is easily and accurately interpreted by search engines.

This gives search engines a more semantic understanding of what entities and topics your page covers, leading to more relevant search results. It also grants you more precise control over how search engines understand your content.

As such, search engines can intelligently display your content to the appropriate users. This targeted visibility can lead to higher clicks, impressions, and click-through rates, ultimately driving better-quality traffic to your site.

To delve deeper into how Schema Markup impacts your SEO efforts, refer to our ‘Common Questions About Schema Markup for SEO’ blog article.

2. Develop a Content Knowledge Graph that supports Generative AI Search

Structured data provides the foundation for developing your organization’s content knowledge graph.

Schema Markup not only describes the entities on your site but also outlines their relationships to other entities on your site and across the web. By implementing robust connected Schema Markup, you are building a content knowledge graph—a reusable data layer that captures relationships between various entities on your site using a standardized vocabulary like Schema.org.

This structured data framework is crucial for training and grounding generative AI search engines and other LLMs, which rely on factual data to mitigate errors and hallucinations.

Gartner has also identified knowledge graphs as a critical enabler for generative AI adoption, further highlighting Schema Markup’s fundamental role in AI advancements.

3. Achieve Rich Results

Implementing certain types of structured data can also enable search engines to display visually enhanced search results (aka rich results) instead of generic “plain blue link” results listings.

Rich results enhance standard search results by presenting additional information, such as a business location, images, product reviews, etc. Rich results are also referred to as enriched results or rich snippets, as they provide snippets of information about a page, brand, or business.

Example of a Product Rich Result

Example of a Review Snippet

Rich results are visually appealing and informative, making your listings stand out on the SERP and improving the overall search experience for users. By incorporating structured data, you cater to both customer needs and search engine algorithms, increasing your competitiveness in search results.

What types of Schema Markup are there?

There are many different types of Schema Markup that you can incorporate into your online content. Some of the most commonly used markup types include:

• Reviews: This type is used to mark up reviews for products, services, or other items. It includes properties like the reviewer’s name, rating, and review text.
• Product: Product markup is used for e-commerce sites to describe specific products. It includes details such as name, description, price, availability, and more.
• Local Business: This markup type is ideal for businesses with physical locations. It includes properties like name, address, phone number, opening hours, and geographical coordinates.
• Person: Person markup is used to describe individuals, including properties such as name, job title, contact details, and social media profiles.
• Organization: Similar to local business markup but broader, organization markup can be used for any type of organization, including corporations, educational institutions, non-profits, etc. It includes details like name, logo, contact information, and social profiles.
• Event: Event markup is used for marking up events such as concerts, workshops, or conferences. It includes properties like event name, date, location, and ticket information.
• Media Objects (images, videos, audio): Markup for media objects like images, videos, or audio files. It can include properties such as caption, thumbnail URL, and duration.
• Creative Works (movies, books, music, TV series, recipes): This type covers a range of creative content, including movies, books, music, TV series, and recipes. It includes properties specific to each type, such as author, director, actors, duration, and ingredients for recipes.

Choosing the right schema type depends on the nature of your content. Each type comes with its own set of properties that you can use to provide detailed and structured information about the content on your webpage. You can view the full list of Types here and learn more about the Schema.org vocabulary here.

What is the recommended format for implementing Schema Markup?

The most commonly used formats for implementing structured data are JSON-LD, microdata, and RDFa. However, Google recommends using JSON-LD due to its readability for both humans and machines.

Implementing JSON-LD (JavaScript Object Notation for Linked Data) is easier than implementing other formats like microdata or RDFa, and you can seamlessly incorporate it within the HTML of your web pages. This format is favored for its simplicity and effectiveness in conveying structured data to search engines.

How to implement Schema Markup on your site

Manually generate your own code and paste it on your site

One way to implement Schema Markup is to do it manually using the following steps.

1. Review Page Content: Examine each page on your site and identify what the page is mainly about.
2. Choose Schema Types: Select the appropriate schema type and properties that best describe the content on your page.
3. Write the JSON-LD: Create the Schema Markup in JSON-LD using the chosen schema types and properties.
4. Embed JSON-LD in HTML: Incorporate the Schema Markup JSON-LD into the HTML of your webpage.
5. Test Markup: Use tools like Google’s Rich Results Testing Tool (if you aim to achieve a rich result) or Schema.org’s Schema Markup Validator to validate and ensure correct implementation and desired results.

If you have the technological savvy to write the JSON-LD and can meticulously work through each page of your site’s content, this approach is a viable option.

However, the manual method of implementing Schema Markup is incredibly labor-intensive—especially if you have a huge website. Similarly, bringing in your own IT team to write and deploy the code can also be costly and time-consuming.

Use a Schema Markup Plugin

Instead of implementing the markup manually, you can opt to use a Schema Markup plugin to implement Schema Markup programmatically.

There are many Schema Markup plugins available for WordPress, Shopify and other CMSes that will allow you to add markup to your page programmatically. However, many Schema Markup plugins tend to be limiting in terms of the Schema type and properties you can leverage. You will also have little control over marking up each page.

Learn more about the pros and cons of using a Schema Markup plugin here.

Hire a Schema Markup Expert

The best way to implement advanced custom Schema Markup would be to hire a Schema Markup solutions provider like Schema App. At Schema App, we provide an end-to-end Schema Markup Solution through our leading semantic technology platform and a team of experts.

Our platform includes tools like our Highlighter and Editor that allow users to generate, deploy, and manage their structured data at scale. Our team of Schema Markup experts will also help you manage your structured data from strategy to results, deploying Markup at scale without diverting your in-house IT resources.

You can focus on your core marketing activities and trust us to deal with the complexities and nuances of your structured data.

Navigate the Complexities of Schema Markup with Schema App

Whether you choose to implement Schema Markup independently or require a solution like Schema App to expertly manage your organization’s Schema Markup, leveraging structured data offers a myriad of advantages for your SEO, AI, and semantic strategies.

If you need help implementing a robust Schema Markup strategy for your site, get in touch with us today.

The post What Is Schema Markup? A Guide to Structured Data SEO 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.

At Schema App, the ambiguity around the use of the Physician type has long been an area of contention for many of our healthcare customers. Therefore, this article will hone in on the issues around Physician Schema Markup and how the new clarifying subtypes will resolve them.

The Issue With Physician Schema Markup

Prior to Schema.org’s version 24 release, the Physician type was intended to represent a doctor’s office. It was a subtype of MedicalBusiness via both Organization and Place, and MedicalOrganization via Organization, as shown in the hierarchy below.

• Thing > Organization > LocalBusiness > MedicalBusiness > Physician
• Thing > Place > LocalBusiness > MedicalBusiness > Physician
• Thing > Organization > MedicalOrganization > Physician

This hierarchy emphasized the categorization of Physician markup as a business with a physical location.

However, naming the type “Physician” caused confusion regarding how to use it properly. As a result, the Physician type was usually applied to describe an individual physician rather than a physician’s office with a physical location.

You can read more about the issue in this GitHub ticket.

Changes to Physician Type in Schema.org v24

Therefore, Schema.org made a few changes to the Physician type in v24 to provide users with greater clarity in their categorization.

1. Redefined the Physician type

The Physician type is now defined as ‘an individual physician or a physician’s office considered as a MedicalOrganization’.

2. Removed Physician as a subtype of LocalBusiness

The Physician type is now exclusively a subtype of MedicalOrganization.

3. Added usNPI property to the Physician type

The Physician type now also includes the usNPI (National Provider Identifier) property, a unique 10-digit identification number issued to healthcare providers in the United States by the Centers for Medicare and Medicaid Services.

4. Introduced two new Physician subtypes: IndividualPhysician and PhysiciansOffice

Schema.org added IndividualPhysician and PhysiciansOffice as subtypes of Physician to disambiguate between these two interpretations of the Physician type.

Note: Schema.org version 26.0 later went on to restore PhysiciansOffice as a subtype of MedicalBusiness, after accidentally omitting it from the v24 update.

You can read more about it in this GitHub ticket.

5. Added occupationalCategory property to the Physician type

The occupationalCategory property is used to describe a job, preferably using a term from a taxonomy such as BLS O*NET-SOC, ISCO-08 or similar. This means you can specify whether a Physician (or its subtypes) has a specific occupational category like obstetrics or pediatrics.

6. Added new practicesAt property to the IndividualPhysician subtype

We will expand on this more in the section below.

But before that, let’s learn more about the two new Physician subtypes and how to use them.

Using the IndividualPhysician Subtype

The IndividualPhysician subtype should be used to describe an entity that is an individual medical practitioner.

The IndividualPhysician subtype still has properties available for things like:

• address
• hospitalAffiliation
• availableService

However, it also has the new ‘practicesAt’ property, which is unavailable to either Physician or PhysiciansOffice.

The practicesAt property should be used to indicate the MedicalOrganization (i.e. hospital, clinic, pharmacies, etc.) where this individual physician practices. If you have the MedicalOrganization entity defined on a different page of your site, you can connect both entities using this property.

Using the PhysiciansOffice Subtype

The PhysiciansOffice subtype should be used to describe an entity that is a doctor’s office or clinic.

It has the exact same properties as the Physician type, so you can still associate the PhysiciansOffice with a location using the address and hospitalAffiliation properties.

Should you Update Your Physician Markup?

In summary, the changes made to the Physician type in v24 will allow users to describe their content with greater specificity and help search engines better understand and contextualize the content on a page.

If you would like to continue using the Physician type to describe the entity on your page, you can do so since the new subtypes have almost the exact same properties. However, being specific with your Schema Markup is essential for search engines to disambiguate the entities on your site, thus allowing them to provide users with more accurate and relevant search results.

Therefore, we recommend updating your markup if you want to:

• Clearly distinguish between an individual physician vs a physician’s office,
• Leverage the practicesAt property, which is only available under the IndividualPhysician type.

The Schema App platform already supports Schema.org V24 if you want to try out the new Physician subtypes and properties. If you are a Schema App Enterprise customer, chat with your Customer Success Manager if you have questions about using these new types and properties.

If your healthcare organization is in need of healthcare schema expertise, we can help!

At Schema App, we provide an end-to-end Schema Markup solution for healthcare organizations and other enterprise SEO teams. Contact us to learn more about our solution.

The post Schema.org V24.0 Release: Changes to Physician Schema Markup appeared first on Schema App Solutions.

In this article, we dive into what Product Schema Markup is, its benefits, and how you can achieve greater visibility and engagement on search by leveraging it on your product web pages.

Expanding on its multiple benefits, adding Product Schema Markup can help your eCommerce website in two ways:

1. Product Schema Markup enhances how your store and products appear on the search engine results page (SERP).

These enhancements, formerly called rich snippets and now known as rich results, can include star ratings, reviews, price, availability, and much more!

You’re probably familiar with star ratings and reviews, as they really stand out in search results as they do in the following example for Ariat. In addition to ratings and reviews, Product rich results can also highlight shipping and return information.

2. Product Schema Markup provides context for the content on your web pages so search engines can better understand and match your products with a user’s search intent.

Schema Markup has benefits that extend beyond achieving rich results for your products and services in search. Through Schema Markup, you can define objects on your web pages as distinct entities with their own properties and relationships to other entities. Once defined, you can connect these entities to a search engine’s knowledge graph, which streamlines your content to be matched with a relevant search query.

For example, if your eCommerce store sells vegan snacks and alternatives, your structured data markup informs Google that these snacks are products for sale and that it’s not, for example, a blog post about the best vegan alternatives to snack on.

Distinguishing Between Product Snippets and Merchant Listings

According to Google, there are two classes of Product rich results: Product snippets and merchant listing experiences.

Merchant listings serve as an extension of the product snippet item, providing more comprehensive search results that consistently feature a price. A carousel may showcase these listings alongside similar products from various sellers or within a knowledge panel in the SERP.

While Product rich results do not appear in the shopping tab, merchant listings do. Notably, they manifest differently within the shopping tab. Incorporating merchant listings allows you to customize your approach as you enhance your target product with additional properties. This is a process that requires the integration of Product markup.

Merchant listings come with a broader set of recommended properties compared to product snippets. These expanded features allow you to segment results based on factors such as seller, brand, pattern, size, and more.

The properties required and recommended for merchant listings are more exhaustive, providing a more detailed and nuanced representation. For example, product snippets don’t require an image, but merchant listings require one.

The effectiveness of merchant listing experiences hinges on specific product data, such as price and availability. It’s important to note that only pages that directly support the product purchases are eligible for merchant listing experiences; pages containing links to other sites selling the product do not meet the criteria.

For reference, see the following example of another Ariat product that achieved an enhanced merchant listing. Notably, it has price listed, a large and clear image of the product, delivery information, ratings, and shipping information.

Product Result Reporting

Each type of rich result—product snippet and merchant listing—comes with distinct enhancements and reporting, each adhering to its own set of requirements and recommendations.

As per an announcement from Google Search Central, they conveyed through a tweet that, “In January 2024, [GSC] will stop reporting the Product results search appearance, both in the Performance report and the API”.

This decision to deprecate Product results aligns logically with the prior split into merchant listings and product snippets. Given that Product results essentially represent a combination of the two, the decision to deprecate it is a move towards more detailed and nuanced reporting for each.

Required and Recommended Properties for Product Structured Data

Google maintains documentation that explains what is required for “Product” structured data.

We’ve captured the most common required and recommended fields below. It is important to keep in mind, however, that the requirements and recommendations may differ between Product Snippets and Merchant Listing eligibility.

For an exhaustive list of requirements and recommendations for both Merchant Listings and Product Snippets, visit the Product Information section in their Structured Data Documentation for Product.

You can see in the example below that you can toggle between the specific properties for “Product Snippets” and “Merchant Listings” exclusively.

You must populate the required properties in order for your content to be eligible for display as a rich result in search. Recommended properties add more information to your structured data, which can provide a better user experience.

Looking for additional guidance implementing Product structured data? Read our article “6 Common Product Rich Result Mistakes You Might be Making” for more tips.

Product

https://schema.org/Product

It’s important to note that Product Structured Data requires only one of the following properties:

• Review
• aggregateRating
• Offers

Once you fulfill one of these requirements, the remaining properties will become recommended rather than required. That being said, it is always best to markup all three properties as they can provide more information in the rich result.

TIP! Add Review, aggregateRating, and Offers properties to provide more information in the rich results.

We created the following visual to help conceptualize the structure of Product Schema Markup. With Product as the starting point, the required properties are used to connect to information in the form of text, URLs, or other data items containing their own properties.

The required and recommended properties for the Review, AggregateRating, and Offer data items are as follows:

Review

https://schema.org/Review

AggregateRating

https://schema.org/AggregateRating

*Note: You must have at least one of ratingCount or reviewCount.
**Note: only required if the rating system is not a 5-point scale (1 = worst rating, 5 = best rating)

Offer

https://schema.org/Offer

TIP! While itemReviewed is required for standalone Review and AggregateRating data items, these should not be used when embedded within the Product template.

FYI: For the most current guidelines on required and recommended fields, reference the Google Developers Reference Guide.

How to Create Product Structured Data

There are two types of pages where you would typically create Product structured data:

1. A product page listing a single product and
2. A shopping aggregate page listing a single product with information from other sellers offering that product.

Learn more in Google’s Product structured data documentation.

To help you get started, we have compiled the fundamental steps for creating Product Structured Data:

Step 1: Add Required Properties for Product Structured Data

Add the required Schema.org properties for Product structured data markup using our reference above. We recommend our own tools, the Schema App Editor and Schema App Highlighter, but there are many different options out there.

You should add all of the recommended and required properties, but also ensure you are connecting the entities on your site. For example, if the brand of your product on your website is also your organization, you want to make sure that the “brand” property connects back to your organization’s entity.

The Schema App Highlighter is a product of the brand, Schema App. Therefore, we can nest the Schema App Organization markup under the brand property to reflect the connection between the Schema App Highlighter and Schema App.

{
  "@context": "http://schema.org/",
  "@type": "Product",
  "@id": "https://schemaapp.com/highlighter/#Product",
  "name": "Schema App Highlighter",
  "brand": {
    "@type": "Organization",
    "@id": "https://schemaapp.com/#Organization",
    "name": "Schema App",
  }
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": 4.7,
    "reviewCount": 63, 
  }
}

Step 2: Review your Product Structured Data to ensure it follows Google’s Structured Data Guidelines

Google’s Product structured data feature guide has specific technical guidelines as well as content guidelines.

Your structured data and website content have to adhere to all these structured data guidelines to be eligible for a Product rich result. Read our article to learn How to Optimize Your Content to Achieve Google’s Rich Results.

Step 3: Deploy your Product Structured Data to the Relevant Pages

Once you’ve finished authoring your markup and ensuring your content aligns with Google’s guidelines, it’s time to deploy your markup.

Google recommends using JSON-LD, which is also our favourite format for deployment!

Step 4: Validate your pages to make sure the Structured Data is working

To test that your Product structured data is working properly, you should use:

1. The Schema Markup Validator (SMV)
2. Google’s Rich Results Testing Tool

Using the Schema Markup Validator

The Schema Markup Validator (SMV) was modelled after and has officially replaced Google’s Structured Data Testing Tool (SDTT). Many SEOs still prefer the SDTT, as the SMV only validates your schema.org syntax and does not show your eligibility for rich results.

Using the Rich Results Testing Tool

Google’s Rich Results Testing Tool helps you to see which rich results can be generated by the structured data it contains.

If you’ve done everything correctly, you should start achieving Product rich results for your pages. However, it is important to note that eligibility for a rich result doesn’t guarantee that the rich result will be awarded to your page.

Google’s goal is to present users with the most relevant search results. If they do not deem a rich result to be relevant to the searcher’s query, they will likely present your page as a regular search result.

Step 5: Manage your Structured Data on an Ongoing Basis

As mentioned earlier, adding structured data to your site not only allows you to be eligible for rich results, it also enhances the search engine’s understanding of your content. This enables search engines to provide users with more relevant and accurate search results.

Therefore, it is imperative for you to continue managing your structured data on an ongoing basis even after you’ve achieved a rich result. To maintain your rich result eligibility, you’ll need to ensure the content on your page matches the structured data.

As we shift towards AI search, maintaining your structured data can also help you control how AI search engines interpret your brand and content. Thereby futureproofing your organization’s web visibility and contributing to the development of the semantic web.

Having a dynamic Schema Markup solution like the Schema App Highlighter can help you update your markup whenever the content on your page changes. Get in touch with our team to learn more.

Scaling Your Product Schema Markup

At Schema App, we don’t just focus on achieving Product rich results – we’re dedicated to unlocking the full semantic potential of your content.

By applying Schema Markup to your product pages, you not only make them eligible for rich results, but you also provide clarity and contextual understanding to search engines through your content markup. This approach lets you take charge of how your brand appears in search, improving visibility and enhancing relevance in search results.

Through the powerful combination of our Schema Markup expertise and advanced semantic technology, we empower your digital team to be more agile and effective in their SEO strategy and preparation for the future of AI-driven search.

We’ve helped eCommerce brands such as Avid Technology and Keen Footwear become leaders in the online shopping industry by showcasing their unique value in search with structured data.

If you’re struggling to find a scalable solution to enhance your Product rich results and drive performance, Schema App is here to help. Get in touch with us today.

Frequently Asked Questions about Product Schema Markup

What is Product Structured Data?

Product Structured Data, also known as Product Schema Markup, is code you can add to the backend of your website so that search engines can provide additional information about your products in search through enhanced features like product rich results.

Schema Markup is a standardized vocabulary that uses the properties and types defined at Schema.org, a resource for SEOs created by Google, Microsoft, Yandex, and Yahoo back in 2011.

How do you Create Product Structured Data?

1. Add all of the required Product schema.org properties to your individual product pages. Google recommends using JSON-LD, as do we!
2. Validate your structured data markup using Google’s Rich Results Testing Tool.
3. Deploy your structured data markup, and use the Schema Markup Validator to analyze your schema.org syntax for any errors.
4. Request that Google recrawls your newly marked-up web page using Google Search Console.

How do you Fix Product Structured Data Errors?

Product structured data seems complex because of three common errors that appear for this type of structured data: “offers”, “reviews”, and “aggregate rating” showing up as ‘either “offers”, “review”, or “aggregateRating” should be specified’. To fix this error, you’ll need to use these three schemas in your Product markup. Product structured data requires including either “offers”, “reviews”, or “aggregateRating” in your Schema Markup.

Once one of these has been fulfilled, the remaining properties will become recommended rather than required. It is always best to markup all three properties as they can provide more information in the rich result. Learn more about how to tell if your Schema Markup is working in our guide.

The post Creating “Product” Schema Markup appeared first on Schema App Solutions.

It provides a structured framework from which new knowledge can be gained through inferencing, allowing for the exploration and understanding of complex relationships within diverse datasets.

At Schema App, we map the content on your website to types and properties in the Schema.org vocabulary. As a result, we create content knowledge graphs. Content knowledge graphs are a more specific type of knowledge graph. They have the same structure and function but are built based on the content on your website.

The goal of any form of marketing is communication: you want to communicate your information to the world. Your content needs to be properly understood to connect with the right people at the right time and through the right channel.

This is hard enough to do successfully between people, but it becomes even more difficult when machines become conduits for this information.

Not only must we anticipate the needs and interests of our audience, but this information needs to be translated into a machine-readable, processable, and searchable format.

Industry Use Cases for Knowledge Graphs

If you’re familiar with the concept, it’s probably due to Google’s Knowledge Graph which popularized the term in 2012. However, knowledge graphs have proven to be valuable across various industries beyond the scope of SEO.

Social Media Sites

Meta constructed its own network of interconnected data points from publicly available information on their social platforms. These data points represent real-world entities and their relationships, creating a graph encompassing people, interests, activities, and more.

For example, when a user clicks the “Like” button on Facebook, this information is represented in the form of triples about that user. In the context of a knowledge graph, these triples consist of Subject-Predicate-Object relationships. If a user likes a particular movie (Object), the triple could be (User – Likes – Movie).

These triples expand the knowledge graph, creating new connections between users and their interests, activities, and affiliations. By analyzing the generated triples, Facebook gains insights into users’ interests, hobbies, preferences, and social connections.

As an example, if a user likes multiple posts related to hiking, the platform understands the user’s interest in outdoor activities. With this enriched knowledge graph, Facebook can personalize users’ experiences. Users see content in their feeds that aligns with their interests, creating a more engaging experience.

Advertisers can leverage this data to target specific demographics based on their interests and behaviours.

Cultural Heritage Institutions

Cultural heritage institutions, including galleries, libraries, archives, and museums, grapple with the daunting task of managing vast amounts of unstructured, semi-structured, and structured data that is stored in silos. The absence of efficient applications to manage this information often leads to manual efforts in collecting and processing this data, resulting in high labour costs and outdated information.

To address these challenges, cultural heritage researchers are turning to knowledge graphs as an innovative and dynamic solution. The following illustration showcases a digital cultural heritage management project that has constructed a knowledge graph using data from the Chinese Palace Museum.

Heritage Science Journal, 2023

Additionally, the integration of deep learning algorithms with these knowledge graphs further refines digital cultural heritage management, enhancing visualization capabilities and making it more intelligent and cohesive. These systems working in tandem is one example of the relationship between knowledge graphs and AI.

Enterprises

Enterprises like large consulting firms have harnessed the power of knowledge graphs to optimize information management. With vast networks of consultants, sometimes in the hundreds of thousands, they often face the challenge of matching the right expert to specific projects.

A specific consulting firm with 300,000 consultants, partnered with SemanticArts to create a knowledge graph detailing each consultant’s specialization, industry experience, skills, and availability. To streamline access to this data, SemanticArts also prototyped a chat service to leverage the knowledge in the graph database.

This is only one example of knowledge graphs being used to standardize and unify information for a chat service. Chatbots act as translators, allowing users to query knowledge graphs in natural language, rather than having to learn graph query languages like SPARQL. This seamless integration of knowledge graphs and chatbots not only enhances internal processes but also exemplifies the potential of this technology in modern enterprises.

With the introduction of LLMs, many enterprises are also exploring how knowledge graphs can be used to ground them to provide accurate and efficient information to users on their sites.

Google’s Knowledge Graph

Arguably the most well-known use case, Google’s Knowledge Graph began with Freebase, a project started by Metaweb in 2007. Freebase was a huge collection of structured data described as “synapses for the global brain.” It became a significant linked open data project in 2008.

Google acquired Freebase in 2010, incorporating this extensive knowledge base into its proprietary Knowledge Graph. Upon releasing their knowledge graph, Google introduced the concept of “strings not things”, announcing their pivot from lexical, keyword-based search, to semantic search.

Google continued to maintain Freebase before giving its content to the Wikidata community in 2014. When Freebase became read-only in 2015, it held over 3 billion facts about nearly 50 million entities.

Google embraced the knowledge graph to harness the intricate connections between entities on the web, adding crucial context to web data.

Knowledge Panels: Google’s Knowledge Graph Brought to Life

Now that you know the history and purpose of Google’s knowledge graph, it’s important to be able to identify how this information is presented in search. Enter – Google’s Knowledge Panel.

The Knowledge Panel is the box that appears on the right side of the search results page when you search for well-known entities (celebrities, businesses, landmarks, etc.).

The Knowledge Panel provides a snapshot of information about the entity you searched for, sourced from Google’s Knowledge Graph. It offers a quick overview, including key facts, images, links to official websites, and sometimes interactive elements like maps or social media profiles.

For example, here is our co-founder, Mark van Berkel’s knowledge panel.

In essence, Google’s Knowledge Graph is the extensive database that powers the Knowledge Panel. It is a compilation of information from one or more pages that presents answers to search queries in the rich results, enhanced SERP results, and knowledge panels we’ve come to associate with authoritative and trusted content.

Even on a smaller scale, organizing data and establishing clear connections between entities is vital for search engines and machines to understand your content.

The good news is that you, too, can manage your content and entities as a knowledge graph for better search engine comprehension. By doing so, you can increase the likelihood of earning a knowledge panel for your business to build authority in search.

However, before you dive into this journey, it’s essential to implement semantic Schema Markup and establish your content’s entities.

The Semantic Building Blocks of a Content Knowledge Graph

To form a content knowledge graph, your content (i.e. plain text, images, etc.) needs to be available in machine-readable code. In the context of SEO, this code is represented by the Schema.org vocabulary.

This informal ontology, of over 840 types and 20+ properties per type, can be applied to web content in the form of Microdata, RDFa, or JSON-LD. Once applied, it is expressed as Schema Markup, and enables machines to understand information about your content and differentiate between things, like local businesses and products.

Moreover, properties can be added to each Type to provide further context to this data. Does the local business serve a specific area? Does the product come in different sizes or colours?

This is information that users were querying about mostly by way of keywords, or “strings”. Schema.org’s ability to define and connect information would turn data into a graph of things and transform the capabilities of search in the process.

Schema.org’s vocabulary has allowed unstructured content (i.e. text, images, etc) to be understood as distinct entities. This provides the foundation upon which knowledge graphs are built.

Developing Your Content Knowledge Graph

By using this markup to then develop your knowledge graph, you are establishing semantic relationships between not only your own content, but to external databases, adding more context to your content.

Developing your own knowledge graph enhances your organization’s ability to manage and utilize its data efficiently, facilitating informed decision-making processes. It also significantly improves user experience by enabling accurate and personalized search results that are directly aligned with user queries. This can lead to more targeted and quality traffic engaging with your site.

But, most exciting of all, is that a knowledge graph promotes interoperability among different data sources and systems, fostering innovation and enabling the development of new products, services, or insights for your organization.

As search is shifting toward AI-powered systems, this innovative approach is becoming increasingly important to stay ahead.

The Knowledge Graph Process

Learning to implement Schema Markup is much like learning another language, one that allows your web content to be understood by machines. Every time you mark up a thing on your page, you are asserting that this thing exists and defining how it relates to other things in the world.

This approach is foundational to the development of a knowledge graph, illustrated in the Data, Information, Knowledge, Wisdom (DIKW) Pyramid below. This hierarchy of information and insight shows that enriching data with context and connection is essential. The more we do so, the more inference and wisdom can be drawn from it.

Data

Whether you’re running an automotive business, a medical office, or a software company, if you have an online presence, you have data. You may have internal data like sales and inventory, and external data like the content on your website. Your content contains data about the products or services provided, location information, and blog content that demonstrates your areas of expertise.

Data is raw and simple, and in this state often lacks semantic significance.

Information

However, simply creating content is not enough. Machines have a harder time interpreting plain texts or images than most humans.

Without structuring this data about your business, machines struggle to interpret it accurately. To bridge the gap between human understanding and machine comprehension, it’s crucial to structure your data to transform it into machine-readable information.

This is where an ontology, like Schema.org, comes in. By applying the Schema.org vocabulary to your data, it becomes structured and subsequently has the potential to become connected. Data becomes information when it is related to other data. For example, the text “Mark van Berkel” on its own is a data point, but it doesn’t give us useful information.

But if the Schema.org vocabulary were used to express that Mark van Berkel is the name of a Person who knows about Semantic Technology and the Semantic Web, and is the founder of Schema App, this provides useful information about the entity Mark van Berkel as an entity that machines can more readily comprehend.

Finding which information to connect to other information can be tricky. This is another factor we take into account in our tools. Because of our background in semantic technologies, we are passionate about connecting your content.

This is why we developed the Schema Paths Tool to simplify the process and save time. The Schema Paths tool provides different pathways for how two entities on your site might connect using the Schema.org properties and types.

By simply inserting the two Types you want to connect, you can see every possible predicate to connect them. You can then choose the one that most appropriately articulates their relationship in the knowledge graph of your content.

Knowledge

However, applying an ontology can only get you so far.

In an interview with Steve Macbeth of Microsoft, he notes that “Semantics without the ability to connect to other data is almost as valueless as no semantics…[S]emantic data [is] only valuable in my opinion when it can be bridged to other data.”

This encompasses the “Knowledge” portion of this process, which further evolves the information that has been distinguished within your content. It represents a collection of information that is useful, typically in the form of triples connected to other triples. This essentially builds a knowledge graph by connecting it to other knowledge graphs.

All of these connections represent contextual information about a particular topic. For example, we can express that Mark knowsAbout the Semantic Web by connecting his knowledge graph to Semantic Web, which is an entity defined by Wikidata.

By linking to other knowledge graphs like Wikidata, we can be more explicit with machines about our information so that they can disambiguate the information about the entity. Your content can inform and be informed by other structured data on the web.

Thanks to this collaborative effort, new knowledge may be inferred and accessed through semantic search.

Wisdom

Wisdom, in this sense, takes existing knowledge derived from knowledge graphs and uses it to make inferences, actionable insights, and educated assumptions to generate new knowledge.

Unlike data and information, which reflect the past, wisdom guides present actions and future aspirations, emphasizing the practical application of knowledge.

What Can You Do With a Content Knowledge Graph

The versatility of knowledge graphs provides limitless flexibility in their applications. Despite being a longstanding concept, various industries continue to leverage knowledge graphs as foundational frameworks for organizing information for many different use cases.

Improve Search

By understanding the relationships between different entities, knowledge graphs enable search engines to offer more relevant results and simplify information discovery for users. They enhance navigation with related links and suggested searches, making it easier for visitors to find what they need.

At Schema App, we’ve been implementing semantic Schema Markup and entity linking to help customers develop their knowledge graph. By having a knowledge graph, search engines can better match your page to a user search query. This drives more qualified traffic and increases click-through rates (CTR).

Ground LLMs

If you want to deploy an AI chatbot to assist your customers, it’s critical that it provides accurate information every time. The problem, however, is that LLMs can’t fact-check like humans do. They respond to queries based on patterns and probabilities, sometimes resulting in incorrect or fabricated responses, known as “hallucinations.”

This means the AI needs to understand specific things about your business, like what you sell and how things are related, to give the right answers to customers. Businesses can address this challenge by utilizing their own knowledge graph, containing accurate information about their products or services, to train the AI chatbot effectively.

The pros and cons of Knowledge Graphs vs. Large Language Models. Image source: Unifying Large Language Models and Knowledge Graphs: A Roadmap

Analyze Your Content

Formatting and structuring your content in the form of a knowledge graph allows you to categorize and quantify your content library and identify gaps. It also enables a deeper level of analysis to answer questions like:

• Which schema.org Types are used most often?
• How many entities are identified on each page?
• How many properties are being used?
• How much of your content is being marked up?

Your knowledge graph can also be used to easily assess your content qualitatively, by answering questions such as:

• How do your entities compare to the search terms you’re targeting with your content?
• Are you linking to entities from external authoritative knowledge bases? If so, which ones?
• Are there any missing that should be present considering your area of expertise?

The Importance of Knowledge Graphs in SEO

Knowledge graphs establish semantic relationships between different pieces of information. They enhance the overall user experience and credibility of a website, which are also factors considered by search engines in their ranking algorithms. By leveraging knowledge graphs, websites can enhance their SEO efforts, leading to increased organic traffic and improved online presence.

For SEO purposes, knowledge graphs are invaluable for delivering precise, tailored search results that go beyond just the string of words typed into search. Empowered by knowledge graphs, search engines can now infer context around a query and fill in gaps that would otherwise remain limited to the keywords used in the query.

This deeper comprehension ensures highly relevant search results, increasing the likelihood of user clicks and qualified traffic, and ultimately boosting CTR for relevant pages.

Developing your own marketing knowledge graph is essential for optimizing your semantic SEO strategy. It helps in preparing your content for the future of search and driving meaningful traffic to your pages.

Interested in developing your own content knowledge graph for your organization but don’t know where to start? Schema App takes care of the technical aspects so that you can take full advantage of what structured data can do without getting mired in the weight of the work.

Contact our team today to get started.

The post What is a Content Knowledge Graph? appeared first on Schema App Solutions.

Schema Markup, also known as structured data, is a code you can add to your site to help search engines better understand the content and entities on your website.

However, Schema Markup is not a one-and-done strategy. As with any SEO initiative, the circumstances around what makes good Schema Markup are constantly changing. Therefore, it is necessary to manage the markup on your site on an ongoing basis to ensure it remains accurate and healthy.

Whether it’s content changes on your page, Google modifying properties for rich result eligibility, or even the evolution of the Schema.org vocabulary, it is critical that your Schema Markup best supports your search objectives, which often requires revisiting the markup across your site.

Let’s dig into some of the strategies our Schema App team uses to manage our customers’ Schema Markup for long-term success.

Verify That Your Markup is Deploying Properly 

Authoring your markup is just the beginning. You need to add it properly to your page and make sure search engines can crawl it for it to take effect.

The good news is that Google provides feedback through triggered emails from Search Console. These emails identify errors or warnings with your structured data that impact rich result eligibility. But why wait for Google to notify you?

If you want to be eligible for a rich result as soon as possible, the best approach would be to test your Schema Markup during the implementation stage – before Google picks up on your new enhancements.

Test your markup using the Rich Results Testing tool and Schema.org Validator

After authoring your markup and gaining access to the JSON-LD, you can paste that code into Google’s Rich Result Test and the Schema.org Validator to ensure your markup is accurate when you deploy it. This proactive approach ensures you don’t miss any opportunities to stand out in search.

Once you’ve added the markup to your page, run your page through those same resources again to identify any initial speed issues or crawlability issues that might impact the benefits offered by Schema Markup.

Request indexing on your priority pages

Assuming everything looks good with deployment and you want to get the benefit as quickly as possible, request indexing on your priority pages so that Google can pick up on any new enhancements.

Ensure Your Schema Markup Aligns With Your Content

Your Schema Markup must be reflective of the content on your page to be effective.

To ensure that it provides the appropriate semantic value, you should author your Schema Markup to support the intent of the page. This will require choosing the most appropriate schema Type and utilizing the associated properties within that schema Type.

For example, are you detailing a service your organization offers? If so, you can utilize Service markup to identify where and when this service is available through the areaServed and hoursAvailable properties.

It’s likely that you will eventually edit or modify the content on these pages in some way. This introduces the risk of Schema Drift, where the content on a page may become misaligned with the values stated in your schema properties.

With the Service markup in the example above, if you change the areaServed or hoursAvailable properties, you will need to revisit the markup to ensure it aligns with the actual content on your page.

By neglecting your markup and not aligning it with your content, you’re likely to miss opportunities to provide more context to Google to better align with user intent. What’s worse, is that you could also be sending mixed signals to Google which may impact performance.

At Schema App, we overcome the issue of content not aligning with markup through our Schema App Highlighter tool. With our Highlighter, users can create a Schema Markup template for similar pages. The markup will then map dynamically to certain elements on the page. Therefore, any changes you make to your content will automatically be reflected in the markup.

Learn how the Schema App Highlighter has made it easy for the CAPREIT team to maintain their markup while dealing with fluctuating prices on their listings.

Optimize Content to Improve Rich Result Eligibility 

The semantic value of Schema Markup cannot be overstated, but it is undeniable that rich results provide a visual element in the SERP that draws user attention and potentially improves CTR.

As defined in our What is a Rich Result article, “a rich result (formerly known as a rich snippet) is an enhanced search result displayed on Google search engine results pages (SERPs) that can be achieved by implementing the appropriate structured data (aka Schema Markup) on your site.”

The key element in that definition above is to implement the appropriate structured data, as each rich result has different requirements. Google’s documentation on Recipe rich results, for example, is essentially a cheat sheet for content ideas and it is worthwhile to review these types of resources to identify missed content opportunities.

Though Google has also changed where and when FAQ rich results will be awarded, FAQ demonstrated another common opportunity where including a single question and answer would show as a valid enhancement. However, Google would only award this rich result to pages where a minimum of two questions and answers were included.

Particularly in blog posts or articles, we would commonly see only a single question and answer included in the content. This presents an opportunity for content teams to optimize the content by introducing an additional question and answer to pursue rich result eligibility. Read Baptist Health’s case study to find out how they were able to improve their rich results eligibility by optimizing their physician page content.

Even though you might have started off your Schema Markup journey authoring markup based on the content on your page, you can also use it as a content opportunity to identify missing content on your page that would make you eligible for a rich result.

Keep Up With Google and Schema.org Documentation Changes

Both Google and Schema.org are constantly changing. An ever-evolving landscape is familiar territory for most SEOs, but making changes to existing Schema Markup can be a challenging task requiring the efforts of additional resources like Development or IT teams.

Nonetheless, when Google makes changes to rich results, it provides an opportunity for you to improve your content for a more enhanced experience in search.

For example, when the Pros and Cons feature was introduced, it was a change that enabled highlighting specific pros and cons of a product directly in the SERP. This provided the opportunity to revisit editorial Product reviews and related markup to ensure there aren’t any missed opportunities for a more enhanced product rich result.

Similar to Google’s constant updates, the Schema.org vocabulary is frequently changing. These changes might impact the various recommended properties for Google’s rich result eligibility.

For example, the version 13.0 Schema.org release introduced additions to e-commerce return policy markup, which was later reflected in Google’s Product markup rich result recommended properties.

Maintaining an awareness of these changes and utilizing the most current Schema.org properties helps organizations win in search by having a more enhanced appearance and richer markup compared to competitors.

Look for Opportunities to be More Semantic

When you implement Schema Markup, you are effectively informing search engines about the entities on your site and improving your semantic SEO. However, you can be even more semantic by linking the entities on your site to other entities on your site and other external authoritative knowledge bases. Doing so will help you develop your very own marketing knowledge graph that you can then reuse for any AI initiatives you have.

By linking entities on your page to other entities on your site

You can link entities on your page to other entities on your site to explain the relationship between both entities.

For example, if you have a blog post on your site, you might want to tell search engines that this blog post is published by your organization. You can explain that relationship by nesting your Organization markup under the publisher property of the BlogPost markup.

This is one of the many ways you can link entities on a page to other entities on your site.

By linking entities on your page to other external authoritative knowledge bases

As you build expertise working with Schema Markup, you can provide further context to your entities by linking to resources like Wikipedia, Wikidata or Google’s own Knowledge Graph. We often use properties such as areaServed, sameAs and knowsAbout to help increase the search engine’s understanding of all the entities mentioned on our customers’ sites – including external entities like cities or other well-known brand names.

Take, for example, Burger King, a global chain of restaurants where, in Australia, it’s known as Hungry Jack’s. Depending on your audience, identifying this relationship through sameAs properties can help search engines clarify the relationship between Burger King and Hungry Jack’s and support performance for a wider range of user queries.

Identifying opportunities to enhance your markup by linking to external entities leads to a more thorough knowledge graph. This, in turn, provides a variety of opportunities, such as enabling search engines to provide users with more accurate responses to their queries.

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

See What Your Competitors Are Doing

Schema Markup is publicly available information and while comparison can be the thief of joy, it can also be utilized as an opportunity to improve your own Schema Markup strategy. We often perform competitive analysis for our customers to help them understand what their competitors are doing in terms of content and Schema Markup.

This includes identifying net new properties or links to external entities that were not previously considered. Additionally, it can help you discover new rich result opportunities by comparing content.

Sometimes, the ultimate insight from comparing your Schema Markup to that of your competitors is that you’re on the right track. This then allows space for other SEO initiatives to potentially bridge that performance gap.

Review Performance and Make Data-Driven Decisions

One of the benefits of implementing Schema Markup is its positive impact on your organic traffic performance. You can easily monitor the performance of rich results using tools such as Google Search Console and Schema Performance Analytics.

Analyzing Rich Result Performance

Though correct Schema Markup will only have a positive impact on your organic performance, certain rich results might show improved performance or, at times, reduced performance.

There can be a variety of reasons why you might see an overall decline in CTR with certain rich results. For example, is the price of your product outside of most buyers’ budgets? This will allow a user to self-assess and decide whether to click through to your page or not, suggesting a potentially lower volume of clicks.

Outside of the myriad of reasons a certain rich result might reduce CTR, it’s important to determine if those rich results are properly serving the intent of your page.

For a converting page, generating clicks might be your top priority. This presents the opportunity to pivot your rich result strategy and targeting. If the price of your product is too high, consider focusing solely on product reviews.

Alternatively, you can get creative with How-to rich results to demonstrate how that product can help users accomplish a particular task.

Ultimately, it is important to ensure your Schema Markup supports your business objectives, which might mean that the most obvious rich result isn’t always the ideal solution. Therefore, you should always experiment to see which rich results work best for your content and business objectives.

Experiment to See What Works

Implementing markup at scale is a challenge for most companies. There can be a variety of barriers, including access to internal IT development resources. You may need to make a business case to justify access to those resources for sweeping changes to your markup.

In the example above from data-driven decisions, introducing How-to content and markup on a single page, let alone a large volume of pages, could be a daunting task. Starting with a smaller volume of pages might be the best way forward to limit the resources required from Content or Development teams.

You can also consider A/B testing pages where you’ve implemented some of the above recommendations, such as modifying certain rich result targeting or implementing linked entities.

Sometimes an unwelcome change might be introduced by Google, requiring a pivot to maintain consistent performance across certain pagesets. As discussed in our Changes to FAQ & How-to Rich Results from Google article, the good news is that change can spark innovation.

While FAQ may not be as worthwhile for rich result targeting, consider experimenting with linked entities, different rich results, or even just expanded markup. Find a tactic that provides maintained performance on your priority pages.

Manage Your Schema Markup for Success

There’s a lot to consider with managing your Schema Markup. The above strategies are just some of the ways our Customer Success team supports our enterprise customers to remove the complexity of managing and maintaining their structured data.

By working with Schema App, you have access to a dedicated Customer Success Manager who can provide you with the expertise and help you manage your Schema Markup from strategy to results. That way, you can stay current with all the industry trends and Google changes, and receive timely content recommendations to stand out in search.

Our Schema App solution also includes access to our tools, like the Schema App Highlighter, which ensures your markup scales dynamically even as your content changes. We also have advanced features like Linked Entity Recognition to ensure that identified entities in your content are reflected in your structured data.

On top of achieving rich results, semantic Schema Markup can help you prepare for the advent of Google’s Generative Search Experience, which makes a focus on managing your Schema Markup all the more necessary.

Need support strategizing, deploying and managing your Schema Markup? Get in touch with our team today to learn about our solution.

The post How to Manage Your Schema Markup appeared first on Schema App Solutions.

Schema.org is an initiative that emerged in 2011 as a collaborative effort among tech giants Google, Bing, Yahoo, and Yandex to enhance the web experience.

At its core, Schema.org bridges the gap between human language and machine understanding through the creation of a standardized vocabulary, aptly named the Schema.org vocabulary.

The Schema.org vocabulary offers a framework for web publishers to organize and structure data and content on a webpage, effectively translating human language into a structured, machine-readable format.

Supported by all major search engines, the Schema.org vocabulary helps search engines to better understand and contextualize web content.

How Does Schema.org Relate to Schema Markup?

The Schema.org vocabulary can be expressed using different formats, including JSON-LD, Microdata, and RDFa. According to Google, JSON-LD is the recommended format due to its ease of implementation, scalability, and lower likelihood of user errors.

When websites express their content in these formats using the Schema.org vocabulary, it transforms into ‘Schema Markup‘ (aka Structured Data markup).

Schema Markup can be applied to websites to describe their content in a way that search engine algorithms understand. This, in turn, improves the comprehension of your content’s topics and intents, allowing it to be more accurately matched to user queries on search engine results pages (SERPs).

Utilizing Schema Markup not only enhances search engine optimization (SEO) but also enables websites to present more informative content, particularly through SERP features like rich results and knowledge panels on Google search.

What are Schema.org Types and Properties?

Today, Schema.org consists of over 800 Types and nearly 1500 properties. It currently includes specific extensions for automotive, bibliographic, health, and life sciences.

Schema.org consistently introduces new types and properties to stay updated with evolving digital landscapes and user needs. This ongoing expansion ensures that its framework remains relevant and versatile.

When you go to Schema.org’s full Type hierarchy, you can see the list of all the Types, and explore their subtypes in a collapsible accordion format.

What is a Type?

A Schema.org Type categorizes entities as specific kinds of things. For example, the ‘Person’ type is used to represent individuals who are “alive, dead, undead, or fictional”, while the ‘Product’ type represents an entity that is available for purchase.

In the image of Schema.org’s full Type hierarchy below, we can see that ‘Thing’ is at the top since it’s the broadest category. Person is a subtype of Thing, and Patient is a subtype of Person.

Not only does a Type categorize an entity, it also defines which properties can be used to describe that entity, or link it to other entities.

You can click on a Type to see its definition and what properties are available to it. Here’s the page that represents the Person Type.

Each Type has the following pieces of information:

1. Name. The name of the type.
2. Where the Type fits into Schema.org’s hierarchy. In this case a Person is a subtype of ‘Thing’.
3. A definition. This tells you what the Type should apply to. For example, Schema.org’s definition of Person states that it can be applied to ‘A person’ who is ‘alive, dead, undead, or fictional’.
4. Properties. Each Type has a list of available properties to further describe it. In the image below we see that the Person Type is further described with properties like address, alumniOf, and birthDate.
5. Expected Types for properties. Each property also has a Type (or Types) it can connect to. For example, a Person has an address property. This information can either be added as plain text, or it can be used to link to an entity typed as PostalAddress, which has its own page in Schema.org.

What is a property?

Schema.org properties are attributes or characteristics that provide additional details and contextual information about a given entity.

For instance, the ‘Person’ type can use properties like ‘name’, ‘date of birth’, and ‘address’. By using these properties, you are providing machines with specific information about an individual.

These properties describe not only the qualities of the entity, but also its relationships with other entities. They can also establish connections between other entities on a web page or authoritative knowledge bases such as Wikidata, further enhancing the interconnectedness of information.

In the Schema.org vocabulary, all Types start with a capital letter (ex: LocalBusiness) while all properties start with a lowercase (ex: areaServed). This will help you distinguish between Types and properties in the vocabulary.

Describing Entities Using Types and Properties

Let’s say we have author pages on our website and we want to make it easier for search engines to understand who a person is, and what their areas of expertise are. For example, according to Mark van Berkel’s author page, he is an expert in “Semantic Technology” and “Semantic Search Marketing”.

As humans, we can read this block of text and understand that Mark knows about these topics and that he works for Schema App.

A search engine, on the other hand, might have a harder time understanding this through the block of text. But, if we describe Mark as an entity using the Schema.org vocabulary, this block of text can be expressed as a graph of Things with specific connections. Here’s a simplified visualization of what that graph would look like:

We can use the Schema.org vocabulary to make a series of statements that describe Mark.

For example,

• This entity is a Person.
• This Person is named Mark van Berkel.
• This Person works for Schema App.
• This Person knows about Semantic Technology and Semantic Search.

Making the Schema.org Vocabulary Semantic

Neither Semantic Technology nor Semantic Search exist in the Schema.org vocabulary. So, in order to be more descriptive about these entities, we could Type them as generic Things, then use the sameAs property to link them to entities in Wikidata. This allows us to say:

• Mark van Berkel knows about a Thing called “Semantic Technology”.
• This Thing is the same as the Semantic Technology entity defined on Wikidata.

If we want search engines to have access to this information, we have to express our Schema.org statements in a machine-readable format like JSON-LD. It might look something like this:

Once we’ve done this work of “translating” our human-readable content into JSON-LD using Schema.org’s vocabulary, we’ll add it to the HTML of the relevant Author webpage. Now, when a search engine (like Google) crawls that page, it will understand who and what this “Mark” thing is, and how he’s related to other things.

In summary, Types (which represent entities) and properties (used to indicate attributes and relationships) are the building blocks that describe entities on a web page and how they relate to each other in a clear,  interconnected, machine-readable format.

Using Schema Markup to describe and relate the entities in your website’s content, not only improves search engine comprehension but also provides users with more relevant and contextually layered information in their search experience.

Why is Schema Markup Important for SEO?

Schema.org plays a vital role in enhancing your website’s SEO efforts by improving how search engines interpret and display your content.

Here’s why adding Schema Markup to your web pages is important for SEO.

1. Helps Search Engines Understand Your Content & Improve Your Semantic SEO

Schema Markup is like a digital translator that communicates directly with search engines. By implementing Schema.org as Schema Markup on your website, you provide explicit information about your content. This enables search engines to grasp the context and meaning of your content more accurately and improve your semantic SEO.

2. Improves Search Result Accuracy

When search engines better understand your content, they can present search results that are more relevant to users’ queries. This means that your website has a higher chance of appearing in the right searches, increasing your visibility to better-quality traffic.

3. Supports Rich Result Eligibility on Google

Certain types of Schema Markup can make your web pages eligible for rich results on Google. Rich results are more visually appealing and informative, often featuring images, ratings, prices, and other additional details.

These visually enhanced search results can improve click through rates and increase qualified traffic to your page.

Facilitating Human-Machine Communication

As websites continue to leverage the power of Schema Markup, the online experience is likely to continue to evolve into a world of deeper machine understanding, enhanced search capabilities, and content that resonates more profoundly with users and search engines alike.

Looking to leverage the Schema.org vocabulary through the power of Schema Markup? Get started today to learn about our solution.

The post Guide to the Schema.org Vocabulary appeared first on Schema App Solutions.

Since our inception, we’ve been helping organizations across the globe implement connected Schema Markup on their websites to strengthen their SEO strategy.

By implementing Schema Markup, our customers can achieve rich results on search engine results pages (SERPs) and increase traffic to their sites. However, the advantages of Schema Markup extend beyond just obtaining rich results. Schema Markup is a semantic technology with numerous benefits.

In this article, we will explore what semantic technology is and how Schema App leverages it to provide meaningful context and understanding of data.

Understanding Semantic Technology

Semantic technology is a set of technologies, methodologies and standards that provide meaning (semantics) to data. It achieves this by representing relationships between different categories of entities, transforming raw data into knowledge.

By providing context, semantic technology enables machines to better comprehend and interpret data, leading to more intelligent decision-making processes.

Background and Terminology

Before jumping into how Schema App is semantic, it is essential to familiarize ourselves with key terms in the field of semantic technology.

Here is a glossary of terms that will be referenced throughout this article.

RDF (Resource Description Framework)

A framework used to express data as a directed graph using subject-predicate-object statements known as triples.

By combining these triples, vast interconnected graphs of resources can be created. This is done using Uniform Resource Identifiers (URIs).

Uniform Resource Identifier (URI)

A URI is a string of characters that identifies a resource. It provides a consistent way to identify resources across different systems and protocols.

A URL is a string of characters that both identifies a resource and where it’s located on the web. Therefore, a URL is a type of URI.

JavaScript Object Notation for Linked Data (JSON-LD)

JSON-LD is a serialization format for expressing RDF data. In simple terms, it’s one way to describe the subject-predicate-object statements. It also happens to be Google’s preferred format for consuming Schema Markup/structured data.

Ontology

An ontology defines the types of entities that can exist within a dataset, and the properties that describe and connect these entities. Schema.org is an example of a loose ontology, serving as a vocabulary rather than imposing strict logic constraints like other formal ontologies.

Knowledge Graph

A knowledge graph is a structured representation of information that captures the context and connections between entities, their attributes, and the relationships between them.

Schema App uses JSON-LD to express how the Schema.org “ontology” defines the connections in your data (in our case, your content).

As a result, content on your page that states things like “this DeWalt Handsaw is from the brand DeWalt” and you can express that in JSON-LD to help search engines understand that statement.

When you connect multiple entities using these technologies, you are constructing a knowledge graph.

Search engines can then interpret the relationships between entities through these knowledge graph connections, enhancing their understanding of the content on your site. More recently, knowledge graphs are also being explored as a means of grounding LLMs to prevent hallucinations in Generative AI.

So as you can see, these technologies are powerful sources of meaning (semantics) for machines like search engines.

The most foundational resource that both uses and is, in itself, a semantic technology, is Schema.org. Schema App utilizes the Schema.org vocabulary to help our customers translate their content to a language understood by search engines.

What Makes Schema.org a Semantic Technology?

Schema.org was founded in 2011 by Google, Bing, Yahoo and Yandex as a way to translate messy human language into structured, machine-readable language. This language is now supported by all major search engines, improving their ability to match search queries with relevant results.

Search engines have shifted to using semantic SEO to provide more accurate and relevant results to users. Instead of matching keywords in an article to search queries, search engines now understand the meaning (semantics) of the content on a page and identify if the content matches the searcher’s intent and query.

In light of this, Schema.org was developed as a vocabulary of types and properties to clearly describe things on a site and provide context on how these things are connected to each other.

Types

The Schema.org types are organized into a hierarchy, starting with Thing and then providing more specific subtypes from there. Example: Thing, which has the subtype Person, which has the subtype Patient.

Properties

Each type has a list of available properties to further describe it. In the image below, we can see that the Person type can be further described with properties like address, alumniOf, and birthDate.

Expected Types for Properties

Most properties also have specific types they can connect to. For example, a Person can have an address property which states the physical address of where the person is located. This information can either be added as plain text, or it can be used to link to the PostalAddress type which has its own page in Schema.org.

By connecting different Schema.org types on your site through the properties, you are defining the relationships between entities described in the content on your site and helping machines understand it.

At Schema App, we apply the Schema.org vocabulary (a loose ontology) to customer content, expressed in JSON-LD (a semantic technology) so that search engines can explicitly understand connections between things (semantics!).

Machine-Readable Representations of Schema.org 

Under the hood, the individual terms on the Schema.org website also have ”machine-readable definitions…available as JSON-LD, embedded into the term page[‘s] HTML”.

If developers are interested in implementing the vocabulary for their own purposes, Schema.org provides downloadable “Vocabulary Definition Files” available in “common RDF formats” like JSON-LD, Turtle, Triple, or RDF/XML. Here’s a link to what the Schema.org vocabulary looks like as a JSON-LD file.

Schema.org has two interfaces – one for humans to navigate and understand, and another for machines to understand the content within their database. This is a great illustration of how semantic technology works to bridge the gap between human language and machine learning.

By understanding semantic technologies, companies like Schema App are better able to create applications and systems that leverage the best aspects of these technologies. At Schema App, we use these files in the construction of our authoring tools and implementation of Schema Markup. This allows us to organize customer information in a meaningful way to machines.

Schema App’s expertise in semantic technologies is evident in our numerous tools and features.

Schema App Tools and Features That Make Us Semantic

Here are some of our tools and features that make us semantic.

Schema App Editor & Highlighter

The Schema App Editor and Highlighter are two Schema Markup authoring tools created by our team. The Schema App Editor allows SEO teams to generate Schema Markup in JSON-LD and automatically deploy it to an individual web page without writing a single line of code.

All the pages optimized with Schema Markup can be updated and live on the site in minutes with this tool, making it easy to manage. The Editor contains the entire Schema.org vocabulary and creates connected Schema Markup via embedded data items to allow our customers to build out their knowledge graphs.

The Schema App Highlighter is also a no-code Schema Markup authoring tool, but it is for templated pages and dynamic content rather than individual URLs. With this tool, you can automatically apply descriptive Schema Markup at scale to thousands of pages and dynamically update your Schema Markup based on the content on your page.

What makes our authoring tools semantic?

Both the Editor and the Highlighter have the same semantic features, just applied in a slightly different way.

Using the Schema.org vocabulary

The primary feature that makes our authoring tools semantic is how they author markup using the Schema.org vocabulary to express RDF triples (subject-predicate-object statements) in JSON-LD.

These statements (aka semantic triples) can be combined to create huge graphs of interconnected resources using URIs (Uniform Resource Identifiers). Earlier in the article, we saw a simplified version of the triples. The image below is a more accurate representation of the triples, where the URIs are the entities being described in the graph.

By doing so, they leverage Schema.orgs’ means of translating human-readable content to machine-readable content, supporting the extraction of meaning (semantics) from web content.

All of Schema App’s authoring tools are Ontology-driven applications. Therefore, any updates or modifications to the Schema.org vocabularies will be reflected in the tools. For example, if Schema.org introduces a new property for a specific Type, the new property will be available in Schema App’s authoring tools.

Entity Linking Features

The Highlighter utilizes an automated Linked Entity Recognition feature to identify entities on the page and link them to Google’s knowledge graph and Wikidata definitions, while the Editor employs a manually applied Entity Linking Method. Both tools then nest the entities within the Schema Markup.

By using Entity Linking Methods like Linked Entity Recognition, our authoring tools can help search engines better contextualize the topics on your site and align them with the searcher’s query.

Linked Entity Recognition

As previously stated, Linked Entity Recognition (LER) is a powerful feature that can be applied to a Highlighter template to enhance content analysis.

Once applied, this automated process identifies named entities (such as people, places, things, and concepts) in content. It then links them to external identifiers from authoritative knowledge bases (like Wikipedia and the Google Knowledge Graph). These identifiers are automatically embedded within your Schema Markup.

Through the automatic embedding of these identifiers into the Schema Markup, the entities contribute valuable semantic information to the metadata. Consequently, Google and other web crawlers gain a deeper understanding of the content, thanks to the inclusion of well-defined, linked entities. This reduces ambiguity in the interpretation of content and supports more accurate matching to user queries.

For instance, we can say the DeWalt Handsaw is from the brand DeWalt, which is the same as the DeWalt described in this Wikipedia entity.

By linking the DeWalt Handsaw to the corresponding DeWalt entity on Wikipedia, search engines can clearly understand which DeWalt you are referring to.

Advanced WordPress Plugin

Like our other authoring tools, our Advanced WordPress plugin provides markup using the Schema.org vocabulary. The plugin can automatically generate Schema Markup for pages and posts. It also provides users access to our Schema App Editor for further Schema Markup customizations.

The Advanced WordPress plugin also has the ability to map WordPress tags and categories to Wikipedia and Wikidata entities to help search engines better match content with relevant search queries on those topics.

Schema Paths Tool

The Schema Paths Tool is a free tool created by the Schema App team to help users identify the best way to connect and organize different Schema types together within their Schema Markup. This is especially useful when you’re unsure which properties are available to connect two different Schema.org Types.

The Schema App team identified a need for this within their suite of tools because Schema Markup is most beneficial when it’s highly descriptive. One of the best ways to do this is by connecting your Types with the most descriptive property. The Schema Paths Tool helps you narrow down what properties each Type has that allow them to connect to one another (as an “Expected Type”).

For example, the Schema.org Organization type has more than 50 unique properties. If you want to connect an Organization to a Service it provides, you can enter both Types into the Schema Paths tool, and then receive a list of properties that can be used to connect these Types.

The Semantic Nature of Schema App

By embracing semantic technologies, Schema App helps you develop a reusable knowledge graph which also enables machines to better comprehend and interpret website content. When search engines have a clear understanding of what your page is about, it can provide searchers with more accurate and relevant results.

Our passion for semantic technologies doesn’t end with the tools and features currently available to our users. We pride ourselves on working towards a data-centric architecture for our internal data as well (see Semantic Arts Data-centric architecture manifesto) and invest time considering the possibilities of semantic technology and how we can support them.

Interested in learning more about how our tools can support your semantic SEO initiatives? Get started here.

The post What Makes Schema App Semantic? appeared first on Schema App Solutions.

Most organizations are trying to stay competitive by embracing the AI changes in the market and identifying ways to leverage “off-the-shelf” Large Language Models (LLMs) to optimize tasks and automate business processes.

However, as the adoption of generative AI accelerates, companies will need to fine-tune their Large Language Models (LLM) using their own data sets to maximize the value of the technology and address their unique needs. There is an opportunity for organizations to leverage their content Knowledge Graphs to accelerate their AI initiatives and get SEO benefits at the same time.

What is an LLM? 

A Large Language Model (LLM) is a type of generative artificial intelligence (AI) that relies on deep learning and massive data sets to understand, summarize, translate, predict and generate new content.

LLMs are most commonly used in natural language processing (NLP) applications like ChatGPT, where users can input a query in natural language and generate a response. Businesses can utilize these LLM-powered tools internally to provide employees with Q&A support or externally to deliver a better customer experience.

Despite the efficiency and benefits it offers, however, LLMs also have their challenges.

LLMs are known for their tendencies to ‘hallucinate’ and produce erroneous outputs that are not grounded in the training data or based on misinterpretations of the input prompt. They are expensive to train and run, hard to audit and explain, and often provide inconsistent answers.

Thankfully, you can use knowledge graphs to help mitigate some of these issues and provide structured and reliable information for the LLMs to use.

What is a Knowledge Graph?

Gartner’s “30 Emerging Technologies That Will Guide Your Business Decisions” report, published in February 2024, highlighted Generative AI and Knowledge Graphs as critical emerging technologies companies should invest in within the next 0-1 years. 

A Knowledge Graph is a collection of relationships between things defined using a standardized vocabulary, from which new knowledge can be gained through inferencing. When knowledge is organized in a structured format, it enables efficiencies in the retrieval of information and improves accuracy.

For instance, most organizations have websites that contain extensive information about the business, such as its products and services, locations, blogs, events, case studies, and more. However, the information is unstructured, because it exists as text on the website.

You can use Structured Data, also known as Schema Markup, to describe the content and entities on each page, as well as the relationships between these entities across your site and beyond. Implementing semantic Schema Markup can:

• Help search engines better understand and contextualize your content, thereby providing users with more relevant results on the SERP
• Help your organization develop a reusable content knowledge graph. This graph can provide valuable structured information to enhance your business’s capabilities with LLMs.

Using an LLM to Generate your Schema Markup

To develop your content knowledge graph, you can create your Schema Markup to represent your content. One of the new ways SEOs can achieve this is to use the LLM to generate Schema Markup for a page. This sounds great in theory however, there are several risks and challenges associated with this approach.

One such risk includes property hallucinations. This happens when the LLM makes up properties that don’t exist in the Schema.org vocabulary. Secondly, the LLM is likely unaware of Google’s required and recommended structured data properties, so it will predict them and jeopardize your chances of achieving a rich result. To overcome this, you need a human to verify the structured data properties generated by the LLM.

LLMs are good at identifying entities on Wikidata. However, it lacks knowledge of entities defined elsewhere on your site. This means the markup created by the LLM will create duplicate entities, disconnected across pages on your site or even within a page, making it even more difficult for you to manage your entities.

In addition to duplicate entities, LLMs lack the ability to manage your Schema Markup at scale. It can only produce static Schema Markup for each page. If you make changes to the content on your site, your Schema Markup will not update dynamically, which results in schema drift.

With all the risks and challenges of this piecemeal approach, the Schema Markup created by the LLM is static and unconnected for a page—it doesn’t help you develop your content knowledge graph.

Instead, you should create your Schema Markup in a connected, scalable way that updates dynamically. That way, you’ll have an up-to-date knowledge graph that can be used not only for SEO but also to accelerate your AI experiences and initiatives.

Synergy Between Knowledge Graphs and LLMs

There are three main ways of leveraging the content knowledge graph to enhance the capabilities of LLMs for businesses.

1. Businesses can train their LLMs using their content knowledge graph.
2. Businesses can use LLMs to query their content knowledge graphs.
3. Businesses can structure their information in the form of a knowledge graph to help the LLM function more effectively.

Training the LLM Using Your Content Knowledge Graph

For a business to thrive in this technological age, connecting with customers through their preferred channel is crucial. LLM-powered AI experiences that answer questions in an automated, context-aware manner can support multi-channel digital strategies. By leveraging AI to support multiple channels, businesses can serve their customers through their preferred channels without having to hire more employees.

That said, if you want to leverage an AI chatbot to serve your customers, you want it to provide your customers with the right answers at all times. However, LLMs don’t have the ability to perform a fact check. They generate responses based on patterns and probabilities. This results in issues such as inaccurate responses and hallucinations.

To mitigate this issue, businesses can use their content knowledge graphs to train and ground the LLM for specific use cases. In the case of an AI chatbot, the LLMs would need an understanding of what entities and relations you have in your business to provide accurate responses to your customers.

Using the Schema.org Vocabulary to Define Entities

The Schema.org vocabulary is robust, and by leveraging the wide range of properties available in the vocabulary, you can describe the entities on your website and how they are related with more specificity. The collection of website entities forms a content knowledge graph that is a comprehensive dataset that can ground your LLMs. The result is accurate, fact-based answers to enhance your AI experience.

Let’s illustrate how your content knowledge graph can train and inform your AI Chatbot.

A healthcare network in the US has a website with pages on their physicians, locations, specializations, services, etc. The physician page has content relating to the specific physician’s specialties, ratings, service areas and opening hours.

If the healthcare network has a content knowledge graph that captures all the information on their site, when a user searches on the AI Chatbot “I want to book a morning appointment with a neurologist in Minnesota this week”, the AI Chatbot can deduce the information by accessing the healthcare network’s content knowledge graph. The response would be the names of the neurologists who service patients in Minnesota and have morning appointments available with their booking link.

The content knowledge graph is also readily available, so you can quickly deploy your knowledge graph and train your LLM. If you are a Schema App customer, we can easily export your content knowledge graph for you to train your LLM.

Using LLMs to Query Your Knowledge Graph

Instead of training the LLM, you can use the LLM to generate the queries to get the answers directly from your content knowledge graph.

This approach of generating answers through the LLM is less complicated, less expensive and more scalable. All you need is a content knowledge graph and a SPARQL endpoint. (Good news, Schema App offers both of these.)

1. The Schema App application loads the content model from your content knowledge graph, which would be all the Schema.org data types and properties that exist within your website knowledge graph.
2. Then the user would ask the Schema App application a question.
3. The Schema App application combines the question with the content model and asks the LLM to write a SPARQL query. Note: The only thing the LLM does is transform the question into a query.
4. Schema App application then executes the SPARQL against your content knowledge graph and displays the results or requests as a formatted response using the LLM.

This method is possible because the LLMs have a great understanding of SPARQL and can help translate the question from natural language to a SPARQL query.

By doing this, the LLM doesn’t have to hold the data in memory or be trained on the data because the answers exist within the content knowledge graph, which makes it stateless and a less resource-intensive solution. Furthermore, companies can avoid providing all their data to the LLM as this method introduces a control point to the knowledge graph owner to only allow questions on their data that they approve.

Overcoming LLM Restrictions

This approach also overcomes some of the restrictions of the LLMs.

For example,  LLMs have token limits, which restrict the input and output number of words that can be included. This approach eliminates this problem by using the LLMs to build the query/prompt and using the knowledge graph to query. Since SPARQL queries can query gigabytes of data, they don’t have any token limitations. This means you can use an entire content knowledge graph without worrying about the word limit.

By using the LLM for the sole purpose of querying the knowledge graph, you can achieve your AI outcomes in an elegant, cost-effective manner and have control of your data while also overcoming some of the current LLM restrictions.

Optimizing LLMs by Managing Data in the form of a Knowledge Graph

You can machine learn Obama’s birthplace every time you need it, but it costs a lot and you’re never sure it is correct.” – Jamie Taylor, Google Knowledge Graph

One of the most considerable costs of running an LLM is the inference cost (aka the cost of running a query through the LLM).

In comparison to a traditional query, LLMs like ChatGPT have to run on expensive GPUs to answer queries ($0.36 per query according to research), which can eat into profits in the long run.

Businesses can reduce the inference cost of the LLM by storing the historical responses or knowledge generated by the LLM in the form of a knowledge graph. That way, if someone asks the question again, the LLM does not have to exhaust resources to regenerate the same answer. It can simply look up the answer stored in the knowledge graph.

Unstructured data that the LLM is trained on can also cause inefficiencies in the retrieval of information and high inference costs. Therefore, converting unstructured data such as documents and web pages into a knowledge graph can reduce information retrieval time and produce more reliable facts.

As the volume of data in the hybrid cloud environment continues to grow exponentially, knowledge graphs play a crucial role in data management and organization. They contribute to the ‘Big Convergence,’ which combines data management and knowledge management to ensure efficient information organization and retrieval.

Build Your Knowledge Graph Through Schema App

In summary, the integration of knowledge graphs with LLMs can significantly enhance decision-making accuracy, especially in the realm of Marketing.

The content knowledge graph is an excellent foundation to leverage schema data in LLM tools, leading to more AI-ready platforms. It’s an investment that could pay off handsomely, especially in a world increasingly reliant on AI and knowledge management.

At Schema App, we can help you quickly implement your Schema Markup data layer and develop a semantically relevant and ready-to-use content knowledge graph to prepare your organization for AI.

Regardless of whether you use Schema App to author your Schema Markup, we can produce a content knowledge graph for you. Schema App can capture the Schema.org data from your existing implementation using our Schema App Analyzer to develop your marketing knowledge graph.

Get in touch with our team to find out more about how Schema App can help you build your marketing knowledge graph to enhance your LLM.

The post How to Leverage Your Content Knowledge Graph for LLMs Like ChatGPT appeared first on Schema App Solutions.