For years, SEOs have primarily associated Schema Markup with its ability to enhance the visibility of web pages on search engine results pages (SERPs), by enabling rich results that capture users’ attention.
However, it’s important to recognize that while rich results are a nice benefit of Schema Markup, they don’t fully capture its true value.
The real value of Schema Markup lies in its capacity to provide search engines with a deeper, more semantic understanding of your website’s content. When implemented correctly, Schema Markup allows you to develop your content knowledge graph and take better control of how your content appears in search.
This article will explore how Schema Markup enhances website visibility and search engine understanding of your content through robust knowledge graphs. This, in turn, refines how your content appears for relevant queries with greater accuracy and helpfulness to the user.
Why Rich Results Are Not Enough
Measuring the return on investment from your SEO efforts can be tough. Hence, many SEOs like implementing Schema Markup because they can easily measure the ROI on their Schema Markup efforts through the performance of rich results.
However, implementing Schema Markup solely for the purpose of achieving rich results can be risky due to their ever-changing criteria and eligibility.
Rich Result Volatility
Over the past few years, we’ve seen the performance of rich results fluctuate based on Google’s algorithm changes. This year, Google has also made substantial changes to the rich results available on the SERP and the criteria for achieving certain rich results.
They’ve ceased awarding video rich results to pages that lack video as their primary content and deprecated How-to rich results entirely from the SERP. Similarly, FAQ rich results have been curtailed for most websites, now reserved only for authoritative government and health websites.
These volatile fluctuations and changes can be unsettling for businesses and SEOs who have come to rely heavily on rich results to drive traffic and engagement.
The True Purpose of Schema Markup
While rich results offer visual enhancements and additional SERP information, they play a secondary role to Schema Markup’s core objective.
The main purpose of Schema Markup is to enable search engines to clearly understand and contextualize the content on a page. That way, search engines can better match the content on a page to the searcher’s query, and provide more accurate search results.
Think of Schema Markup as a tool to assist search engines in content comprehension, with rich results being a bonus feature for publishers using specific markups.
By structuring your content with Schema Markup, you’re not just chasing rich results; you’re preparing your content for the future of AI-driven search.
What Else Can You Do With Schema Markup?
By now it’s been made clear that Schema Markup has much greater potential than most have given it credit for. Let’s dive into some of the powerful ways Schema Markup can drive results for your organization and keep you competitive in search as it continues to evolve.
Integrate Your Schema Markup
Once implemented, you can also seamlessly integrate your Schema Markup with other external data sources. This flexibility enables you to provide richer, more comprehensive data experiences in the applications and platforms your business chooses to integrate with.
In addition to integrating it with external data sources, you can also integrate your Schema Markup with internal tools, platforms, or systems. This allows for a more cohesive data management strategy within your organization.
Your Schema Markup can be integrated using APIs or Linked Open Data. For example, an e-commerce website might integrate Schema Markup with their inventory management system via APIs. This would allow the product details (like price, availability, and ratings) to be dynamically updated in real-time based on the Schema Markup.
Another example is integrating through Linked Open Data. A cultural institution, like a museum, might use Schema Markup to describe their exhibits and then integrate this information with global datasets like Wikidata. This would help in providing richer context about the exhibits and potentially drive more visitors.
Reuse Your Schema Markup
Your Schema Markup can be reused in various scenarios. One prime example is with our WordPress plugin feature. By appending ?format=application/ld+json to URLs, you can retrieve the schema for a particular page. This facilitates:
- Mobile Apps: Developers could pull this Schema Markup to display rich content snippets in a mobile app about the company’s services or products.
- Chatbots: Businesses could leverage the schema to answer user queries more accurately, providing detailed information pulled directly from the website.
- Partner Websites: If a business has partnerships with other websites or platforms, they can share the Schema Markup, ensuring consistent and updated information across platforms.
Build Your Knowledge Graph
A knowledge graph is a collection of relationships between the entities defined using a standardized vocabulary, from which new knowledge can be gained through inferencing.
For additional clarity, an entity is a thing that has specific attributes. For example, your postal address is a thing that can be described by the country, region, postal code and street address.
When you implement Schema Markup on your site, you are essentially using the Schema.org Types and properties to describe the entities on your site. Each entity is then identifiable through a Uniform Resource Identifier (URI) to ensure that it can be referenced to other items in your graph.
You can develop a knowledge graph by using the Schema.org vocabulary to connect the entities on your site to other entities on your site and other external authoritative knowledge bases like Wikidata or Wikipedia. By doing so, you are establishing your entity and defining how it connects to other things that exist in the world.
What Makes Knowledge Graphs So Valuable?
At Schema App, we leverage Schema Markup to enable you to present your data in the form of a semantic knowledge graph, but the real magic lies in how you choose to use this connected data.
Your knowledge graph is a versatile resource that opens up a world of possibilities tailored to your specific business objectives.
For instance, you can harness the power of SPARQL Queries to extract precise data and information from your knowledge graph. This capability enables tasks such as generating insightful reports, counting the number of pages related to a particular topic, or tracking external entities linked to your Schema Markup.
These reports not only offer valuable insights but also serve as a foundation for identifying content gaps within your domain. By analyzing your existing content against your knowledge graph, you can determine which topics are well-covered and which areas require further exploration.
This strategy helps you build your authority by pinpointing opportunities for content expansion.
Enhance User Experience with Better Content-Query Alignment
When left to their own devices, search engines rely on natural language processing to parse the information on a site, which can lead to inaccuracies. When the information on your site is organized in a structured knowledge graph using the schema.org vocabulary, it makes it easier for search engines to understand and contextualize your site content.
This leads to more precise matches between your content and search queries, ultimately improving user experience and the quality of traffic you are getting to your site.
Our Customer Success team has even experimented with linking entities on a page to external authoritative knowledge bases like Wikidata and Google’s knowledge graph. This approach has yielded positive results, increasing click-through rates for queries related to those entities.
While it might not necessarily boost the visibility of your pages like a rich result, it does ensure that the clicks are from users who are genuinely interested in your content.
Integrate Your Knowledge Graph
Your knowledge graph can also seamlessly integrate into your workflow, serving as a backbone for various tools and applications.
At Schema App, for instance, our Editor tool relies on the knowledge graph to provide a comprehensive experience. All of the information in that interface is part of our knowledge graph. Any changes made to data items in our tool directly impact and update the knowledge graph.
Additionally, you can leverage your content knowledge graph to build custom web applications. This is accomplished by providing data for new apps and enabling developers to create user interfaces that utilize the wealth of information within your knowledge graph.
Ground and Train Your Internal LLMs
In the realm of AI search engines, one significant challenge is the potential for incorrect inferences leading to hallucinations. Hallucinations occur when Large Language Models (LLMs) making up false information that is not based on real data.
You have the power to mitigate this major risk by using your knowledge graph as a control point to define your content more precisely to AI search engines.
Although major search engines have yet to officially confirm this, there’s potential to train AI search engines to provide more accurate results by grounding their understanding with your knowledge graph.
Another interesting use case for knowledge graphs is that you can reuse them to train your own internal LLMs. An example of this is the use of AI chatbots on your site to address common customer queries.
Grounding your LLMs with a knowledge graph enhances the performance of customer queries. It also ensures the accuracy of the information provided, since the LLM is restricted to the statements (RDF triples) expressed in your knowledge graph.
You can clearly define entities in your content knowledge graph to ground it with factual and accurate information about your organization.
Leveraging the True Power of Schema Markup
As search engines become more sophisticated and semantic, they attempt to grasp the nuances of human language, meaning and intention.
Schema Markup serves as a bridge between your content and these semantic search engines. It enables your content to be interpreted more accurately, leading to improved relevance in search results.
While rich results undoubtedly hold distinctive value and can elevate your content’s visibility, they should be seen as a bonus rather than the sole objective of Schema Markup.
Schema Markup’s true value lies in its ability to help search engines understand your content’s context and intent. When you implement Schema Markup with machine comprehension in mind, you not only enhance your chances of securing rich results but also ensure your content remains resilient and relevant in an ever-changing search landscape.
Looking to develop your very own marketing knowledge graph through the power of Schema Markup?
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Mark van Berkel is the Chief Technology Officer and Co-founder of Schema App. A veteran in semantic technologies, Mark has a Master of Engineering – Industrial Information Engineering from the University of Toronto, where he helped build a semantic technology application for SAP Research Labs. Today, he dedicates his time to developing products and solutions that allow enterprise teams to leverage Schema Markup to boost their SEO strategy and drive results.