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SEO Schema

How SEO Schema Helps Solve Disambiguation

Schema markup can give websites advantages well beyond rich results by clarifying what you say and your content’s context.

Schema markup is a great way to overcome brand and content ambiguity challenges by providing additional information and context for your entities. Where statements in your content could be taken one way or another, you can improve confidence and the understandability of your data. It is best to avoid chances of your brand and web statements being misinterpreted; you can ensure accuracy and content usefulness for relevant search query matching.

Voice Search: When a person conducts a query by voice, search engines have to depend heavily on implied context; schema markup can help provide that needed context to an otherwise ambiguous text.

This makes it essential to accurately represent your content with a structured and strategic approach to content optimization.

Like in natural human conversations, schema markup’s clarification provides the meaning of your web pages. As a result, search engine’s understanding is checked, and areas of confusion or misunderstanding can be solved.

SEO schema markup is not just about gaining a rich result.

The Google Search Central team often answers questions about the use and value of structured data implementation for websites. Many center around how to get started with schema markup when using a website builder or new to it. On February 1, 2024, another session went live to explain how to get started.

“(Rich results) While the details differ between them, they are all powered by structured data.

In our gallery of all the structured data we might use for enhancing the search result appearance of a page, you can use the different formats.” – Structured Data for Beginners by Google’s Martin Splitt [1]

We agree that initially, the value of schema markup was largely about gaining rich results. However, it is also a data layer that strengthens the semantic understanding of a web page for search engines providing AI answers. The desire to gain rich results inspired many SEOs and marketing teams to adopt the practice of implementing schemas. But today, its use has matured beyond rich results.

This 2024 recap is hardly new, but it does signal that Google’s reliance on structured data to understand content is on-going. During a 2019 Google Webmaster Central office hours session, Mueller said:

“On the one hand, we do use structured data to better understand the entities on the page and to find out where that page is more relevant. But that doesn’t mean that just because people are doing things in a technically correct way on the website that the page is a better page than it would be otherwise.

We will try to use it (schema) to show it (your page) in more relevant search results that would perhaps bring more users to your pages that actually match the topics of your pages.

But it doesn’t mean that we would show it to more users or that it would rank better.” – John Mueller

Disambiguating Entities Helps Identify Pages More Relevant to Specific Queries

Just two months into 2024, Google has already invested in updating its schema documentation. Several of these updates aim to help business owners and retailers disambiguate complex and important concepts.

Rumors may surface that the need to add essential schema markup formats to web pages has deminished. However, we see opposite signals from Google.

New Jan-Feb 2024 Google schema documentation/support to disambiguate types:

  • February 29 – Additional documentation for structured data carousels (beta for travel, local, and shopping queries).
  • February 20 – Additional support for product variants, including the isVariantOf property to product structured data.
  • February 20 – Provided clarification for product return fees markup: such as when to use FreeReturn versus ReturnShippingFees. We see this statement in the Google documentation as a way to improve vague shipping policies: “Why: To better support more granular shipping and return fee scenarios”.
  • January 9 – Additional support for Product schema suggestedAge as an alternative to suggestedMaxAge and suggestedMinAge. Each lets you disabuguate and clarify possible values for suitable product age ranges.
  • January 9 – Replacing @id references with hashtags. The intent is to use “hashtags as resolvable in-page node identifiers in RDF.”

As quarter four of 2023 wrapped up, Google had introduced six additional rich result opportunities. This surpassed what it had released in previous years. Four out of the six new Google rich results announced in 2023 were schema types for specific industries.

Exceptional structured data SEO opportunities for niche sites:

  • Travel industry
  • Healthcare niche
  • Automobile-related sites
  • Vacation rentals
  • Educational courses
  • Recipe schema
  • Local business schema

Each of the above schema types can be used to potentially improve your website’s visibility and click-through rates even if you’re in a competitive niche.

How to Use Entity Linking Methods

Schema markup lets you link entities so that they are are distinctly defined and distinguishable concepts

Entity Linking techniques include entity recognition, disambiguation, co-reference resolution, and classification. Semantic triples clarify node relationships by tying concepts and entities together. An RDF triple clarifies statements you make in your content whether by concept to concept or concept to property and property value. This way computers and AI can understand and interpret the meanings being described.

It is a crucial method that semantic SEOs can leverage for a data science approach for the identification and connection of entities within text.

Examples of connected schema markup uses:

1. Your company’s about page can use Profile Page schema to differentiate you from similar business entities.

2. Use GSI linked data identifiers to add more details to your products’ landing pages. For semantic models, barcodes with GS1 take product GTIN numbers to a higher level by managing product variant details. This reduces data errors. Semantic similarity signals can be used for finding related products. Semantic similarity helps to discover the relationships among items that are not possible by lexicographic similarity and compares the distance between any two pieces of data.

3. The Organization structured data type informs search engines about the unique services and products your offer.

4. Nested author structured data sets your domain apart with identified topic expert writers. By embedding this code correctly, you can link author biography page to help readers clearly identify who they are.

5. Use the disambiguatingDescription property type. It is describes as “A sub property of description. A short description of the item used to disambiguate from other, similar items. Information from other properties (in particular, name) may be necessary for the description to be useful for disambiguation.”

6. Factual (ClaimReview schema markup), connected schema markup adds to your data layer for machines to consume accurate inferences and statistics in your content.

Key Schema Types that Help Solve Disambiguation

Innovative uses of schemas can turn vague data and underspecified parsing into data that is more easily understood. The benefits of this semantic SEO strategy cannot be overstated.

1. sameAs property

The sameAs schema property connects a “thing” to a URL that serves to explain and identify it. For example, it can provide the extra definition necessary to enable Google’s algorithm to see “nail” as in how you fix a wood structure, a person’s finger nail, or the program “Nailed it!” SameAs adds an understanding of which entity is being referenced. are, in fact, headquartered in Rome NY as opposed to Rome.

It can be used to show that your crunchbase or social profiles are ‘the same as’ you. We also use it often to link to a Wikipedia or Wikidata URL that defines the entity.

2. about property

The about schema property helps expand or explain the subject matter of a CreativeWork or thing. It can signal multiple entities or a content topic. Sometimes “about” is easily implied. Or it can be challenging, even risky. As more people test Generative AI tools, guardrails for AI healthcare answers and financial sites are essential to avoid misinformation crashes.

3. url property

This is the URL of a reference Web page that unambiguously indicates the item’s identity. The URL property helps solve disparate structured data entities with linked data. This data can be mapped to a matrix in a machine-readable format that highlights your most important URLs.

4. @id identifier

While we don’t call this a property, @id helps express JSON-LD to specifically identify data nodes contained within a document. The use of @ids in schema markup lets each key data item be unambiguously identified and referenced by other data points in your schema markup graph.

The ‘value’ of the @id property often is a URI (Uniform Resource Identifier). Consider @id working like a fragment identifier that points to a subordinate resource. Cross-page @id linking is almost definitely worth implementing and is an important task in knowledge graph building. Utilize the same @id consistently across your website.

5. additionalType

Using the additionalType property helps you explain context by referencing external vocabularies like investopedia, Wikipedia, Wikidata, Scholarpedia, and Citizendium. To gain Google Search Feature validation, it is helpful to include relevant additionalType markup on a page. Search engines can be faster and more accurate when piecing linked data for query matching.

6. audienceType

The lets you specify the target group associated with a given audience (like; seniors, veterans, educatiors, dentists, car owners, musicians, etc.). You can also embed geographicArea schema to indicate the geographic area associated with the audience that will benefit from your content.

If your website targets different audiences but through the same keyword, you may be relying on topic groups and multiple web pages. For example, if you are a painting servcie, you may have “residentail” and “commercial” menu tabs. Or a medical device company can benefit from B2B and B2C content within one site.

You can create another domain; they can rank as long as they serve a different user intents or audiences. However, this can become challenging as it often means running into duplicate or near-duplicate sites that add management costs, duplicate content issues, and confusion.

One SEO schema markup solutions is to leverage Audience Schema (MedicalAudience, BusinessAudience, Researcher, EducationalAudience, and PeopleAudience).It can work to deferientate audience much like Language Schema Markup, Service, MedicalScholarlyArticle, NewsArticle, and TechArticle for example.’s audience type is usful to further define the following schema types:

  • CreativeWork
  • Event
  • LodgingBusiness
  • PlayAction
  • Product
  • Service

Create a Sustainable Semantic Schema Markup Strategy

Generative AI search engines are changing the way people consume information. As unpredictable as future SEO strategies are, we know that the basics of mathematical information retrieval remain solid as long as people rely on the Internet to get answers. Schema markup code is the language of machine learning, deep learning, and artificial intelligence.

Powerfully built, connected schema markup gives you remarkable opportunities to tell search engines what’s important in your content. With large language models (LLMs) and generative AI power housing Google’s Search Generative Experience (SGE), ChatGPT, Google’s Gemini, and Bing, you need accurate inferences that set you apart.

Schema helps you expand your content’s usefulness

If Google’s understanding of your content is blurry or unconfirmed, such pages may go unread. Semantic markup lets you do more than jsut define the entities on pages. Additionally, you’re shedding light on their relationship to other entities on both your website and the World Wide Web. An interconnected web of data information for your site is created.

Your content knowledge graph is a knowledge base about your organization. It may supply AI knowledge graph answered questions, Discover results, product carousels, or People Also Search For boxs.

Use AI to Update Schemas in the Google Cloud Console

Google displays clear, helpful, and organized content in SERPs only if it understands the data.

Google Entity Search can have a direct or indirect role in many search queries. That is why you will find different PAA answer box variants in the SERPs for many searches. AI can provide a conversational experience with your end users right on the SERP by pulling your content. For example, clear schema on a web page can feed a Google FAQ agent that answers frequently asked questions.

Google strongly recommends that you update your schema with key property mappings, especially for title. This ensures that your results are displayed correctly and helps Google’s  Vertex AI Search and Conversation identify important information that allows it to generate better results.

Updated Schema helps by providing disambiguation.

If you are using the Google Generative AI App Builder to auto-generated schema, make sure that data ingestion has completed. Check your schema in the Google Cloud console or by using the dataStores.schemas.get API method.

Useful on-page schema describes, clarifies, and tells your content story

Below are a few structured data quotes that support this concept from people I admire in our SEO industry.

Entity Stacking is an SEO process of communicating information about an entity (a thing) to search engines. Since structured data is a standardized format for providing information about a webpage and classifying its content, take advantage of it to clarify, afirm, and get your content cataloged. Below are a few structured data quotes from people I admire in our SEO industry.

These SEO schema tactics help Google identify the concepts in your content (such as entities, attributes, types, and relationships). By connecting your content’s concepts together, you can increase search engines’ confidence in those connections so that they match queries to you more often.

“Structured data (in any form) fuels smarter searches, turning them into real conversations (look at Gemini, when it works) – no AI hallucinations but factual data corroborated from multiple sources. – Andrea Volpini of Wordlift [2]

“I think we’ve entered the heydays of structured data, one that will last longer than many believe. Although as things progress it will evolve from a method for teaching machines about the world to a method of correcting their ‘understanding’ of it.” – Jarno Van Driel[3]

“Schema is metadata—the data beyond (or behind, if you will) the data—that can help Google understand what your content is about. Look at it this way: it’s like a narrator in a movie explaining what is going on that may be unsaid or unclear. Schema is like giving your data a narrator or a voice, if you will, that explains what the content is about.

Semantic SEO is about disambiguating your content as much as possible. Ambiguity can be your biggest stumbling block when trying to rank well as it creates guesswork for Google—and Google is a machine, so it doesn’t make guesses unless it’s confident it’s making the right ones. Disambiguation helps reduce that guesswork and makes things clearer, which can significantly increase Google’s confidence in your content’s topical relevance.” – Michel Fortin on Wix[4]

SEO Schema Helps Disambiguate and Control Your Content’s Narrative

This SEO strategy helps us clearly articulate the unique intent and meaning of individual content pieces. We are excited to see the expansion of the existing web ecosystem where things that are underspecified as strings can become more complex and explicit schema types.

If you would like to partner and take your structured data implementation to a higher level, requst our Schema Markup Audit for Opportunities to Differentiate Your Business


[1] Martin Splitt, “Structured Data for Beginners,” Feb 2024,