These topics were also discussed at length during the keynote presentations at Pubcon Austin 2023. Our CEO, Martha van Berkel was there to hear it and left Pubcon with an insight or two on the future of Schema Markup and AI-powered search.

The relationship between Schema Markup & AI 

This past week, Martha presented at Pubcon Austin 2023 on the Top Ways to Use Schema Markup. During the presentation, she discussed how Schema Markup relates to Artificial Intelligence (AI) and the importance of connected Schema Markup in an era of AI-powered search.

Connected Schema Markup helps build your Knowledge Graph

When you implement connected Schema Markup across your site, you are essentially building a knowledge graph.

“A knowledge graph is defined as a collection of relationships between things  defined using a standardized vocabulary, from which new knowledge can be gained through inferencing.”

Schema.org is the standard vocabulary used by search engines to understand the content on a page and when you use it to define relationships between entities on a website you are creating a knowledge graph for that organization. In fact, when you look closely at Schema.org, you can see how it was build with creating knowledge graphs in mind.

When you look at the Schema.org vocabulary definitions, you’ll often see many properties expecting the value of the property to be another Type instead of a text or URL value.

Using Physician as an example, the Expected Type for the hospitalAffliation property is another schema.org Type called Hospital. By embedding the Hospital Type under the hospitalAffliation property, you are defining the relationship between the Physician and the Hospital that they are affiliated with.

This example showcases how the Schema.org vocabulary defines relationships between things on your website. When you connect and define these relationships using Schema Markup, you are building your knowledge graph.

At Schema App, we know the importance of creating connected Schema Markup and building a reusable knowledge graph. Our enterprise solution utilizes semantic technology that creates and stores your Schema Markup and web content in a knowledge graph.

But what do knowledge graphs have to do with AI and Machine Learning? Why is it important for organizations to develop their knowledge graph using Schema Markup?

We asked ChatGPT why knowledge graphs are important and here’s what it had to say.

“Knowledge graphs are a powerful tool for representing and organizing information in a structured way.

Here are three reasons why knowledge graphs are important:

1. Integration and Interoperability: Knowledge graphs allow for the integration of heterogeneous data from different sources, formats, and languages. This enables organizations to gain a holistic view of their data, making it easier to identify patterns and insights that might not be visible when data is siloed. Knowledge graphs also enable interoperability between different systems, making it easier to share and reuse data across different applications.
2. Semantic Search and Analysis: Knowledge graphs use semantic relationships to represent information, making it easier to perform advanced search and analysis tasks. For example, knowledge graphs can be used to perform complex queries that take into account the relationships between different entities, such as “find all the people who have worked at companies in the same industry as Microsoft.” This can help organizations identify patterns and insights that would be difficult to discover using traditional search and analysis methods.
3. Machine Learning and AI: Knowledge graphs are a foundational technology for machine learning and AI. By representing information in a structured and standardized way, knowledge graphs make it easier for algorithms to extract insights and make predictions. For example, knowledge graphs can be used to train natural language processing models (NLPs), or to build recommendation engines that suggest products or content based on a user’s preferences and behaviours.”

(Source: ChatGPT, Open Ai)

Search engines leverage your Schema Markup and knowledge graph as data sources to train their machines and infer new knowledge. By developing your organization’s knowledge graph, you can prime your organization’s web data to be ‘AI-ready’.

Earlier this year, Ryan Levering, Google’s champion for structured data, said the following with regard to what Google wants from Schema Markup.

Also, over time richer/correct semantics will favour more connected graphs.
– Ryan Levering, Google (Source: Mastodon)

Even though Google has yet to release any official documentation around connected Schema Markup, Levering’s comment indicates its growing importance in the world of search.

Our sentiments on connected Schema Markup were also echoed by Fabrice Canel, Principal Program Manager for Bing, at his keynote presentation in Pubcon Austin 2023.

SEO recommendations for Bing AI Search

During his keynote presentation, Canel offered valuable tips and insights on optimizing for Bing’s new AI search engine. Even though AI search is in its infancy days, Canel shared that one of the ways SEOs can prepare for this new AI-enabled search is by writing great content and annotating with Schema Markup. 

In a later slide, he further elaborated on what they mean by great content and Schema Markup. They specifically mentioned using ‘Semantic markup’ to convey information about the pages. 

Semantic markup is also known as connected Schema Markup, where you define the relationships between the content on your pages and other definitions on the web using the properties defined in Schema.org.

This goes to show that connected Schema Markup is important for AI search engines and SEOs need to invest in it. It is also why our team at Schema App constantly emphasizes it when building a Schema Markup strategy for our customers.

How AI will transform the search experience

During his keynote presentation, Canel also shared about the various types of search queries and how the search engine results will vary to best satisfy the user’s query.

Our takeaway from it is that the new Bing Chatbot experience will suit some queries, while others other queries will be better answered with a table or a version of today’s search results.

For example, for queries such as ‘Tell me all the hotels in the Dominican that have waterslides’, users might be satisfied with a chatbot summary answer and even accept a margin of error.

On the other hand, for queries such as ‘What is the recovery time from a hip surgery”, users might want to read different articles on the subject and personally determine who the subject matter expert is before accepting the answer.

Over time it will be interesting to see how these different experiences and types of searches evolve with this new AI chat technology.

Despite the recent buzz, AI and machine learning are not new to search. Gary Ilyes from Google kicked off Pubcon with his keynote presentation on how AI dates back to the bronze age, how these concepts are already deeply entrenched in how we conduct business, and how they will continue to evolve.

We really enjoyed seeing how existing industries today are using machine learning and AI through the tools and process automation that are already adopted.

However, Ilyes did not comment on Bard or whether Google would be releasing a Chatbot in response to the New Bing so we’ll just have to wait and see.

Schema App & AI

At Schema App, we also utilize AI and machine learning in our tools. We use it for our Linked Entity Recognition feature and our Schema Performance Analytics tool.

Because of our passion for semantic technology, your data is stored in a knowledge graph when you create your Schema Markup with Schema App. We then layer on additional AI capabilities to help you add more meaning to your content.

For example, Schema App’s Linked Entity Recognition, allows our technology to create connected Schema Markup using Natural Language Processing to connect your content to known entities in Google’s Knowledge Graph and Wikidata. This provides context to the content, connecting content using the sameAs link, or more flexibly with mentions, about, category, etc.

An upcoming release will also include a Medical BERT conceptual model that Healthcare companies can use to advertise all their specialties. We’re also working on a feature for Schema Performance Analytics to generate AI insights from the performance data, and will be releasing it in Beta shortly.

Start preparing for an AI-powered search experience

AI and machine learning are here to stay and will continue to gain prominence in the search experience. Thankfully, the evolution within search will not happen overnight. It will likely evolve over the next few years.

However, organizations need to ready themselves for what’s to come. As you adopt, deploy and manage your Schema Markup to achieve a rich result, you also want to ensure that you’re doing it semantically to build a connected knowledge graph. That way, you can lay the foundations to be relevant for search engines and perform well in this new search experience.

As a semantic technology company, Schema App is excited to provide you with the expertise and tools to do this in a scaleable, manageable way with measurable results. If you need help creating connected Schema Markup, get in touch with us today to find out how we can help you prepare for this new search experience.

The post The Future of Search: AI, Machine Learning, and Schema Markup appeared first on Schema App Solutions.

Two key takeaways:

• Search is changing – with Google making updates to the SERP, in Video, FAQ, and Product Rich results.
• Diversify your rich results and manage your Schema Markup to achieve optimal results in changing search landscape. 

Rich Result Changes in September 2022

FAQ Rich Result Changes

This past August, we reported seeing 42% of our customers experience a drop in clicks for their FAQ rich results across mobile and desktop. 

This trend has continued into September as we continue to see 45.88% of our customers drop in clicks for their FAQ rich results in the first two weeks of the month and 43.53% of our customers have continued to see a drop in clicks for their FAQ rich results in the second half of September. To date, we’re seeing this decline affect a wide range of industries. 

Healthcare is one of the industries that took a huge hit. 57% of our healthcare customers saw a further decrease in clicks and impressions this September. Google is no longer awarding the FAQ rich results for Physician pages, resulting in a decline in clicks for their FAQ rich results. However, many of these customers are still getting clicks from the FAQ rich results on their services and blog pages. 

We have yet to identify the cause of the decline for other industries but we do know that Google’s algorithm is constantly changing. FAQ rich results continue to perform, however, organizations should be reviewing their Schema Markup strategy to ensure they have a diversified portfolio of rich results.

This means reviewing the rich results your organization is achieving today and proactively looking for content on your site that is eligible for more types of rich results opportunities. Diversifying your rich results allows your website to keep performing despite Google’s changes and your team can quickly double down when a specific rich result is performing. 

Google preferring Youtube for Video Rich Results

We first observed a sharp decline in clicks and impressions for Video Rich Results in June, where 70% of our customers saw a decrease in the performance of their Video Rich Results on desktop devices. The Video Rich Results started to recover in August until this sudden drop again in September. 

In the first half of September, we saw 41.86% of our customers decline in clicks and a 53.49% decline in impressions for their Video Rich Results compared to the second half of August. 

In addition to the number of customers impacted, we also saw a big change in how Video rich results are showing up in the SERP. Google is preferring Youtube over other providers. 

Could this be a result of Google’s September core update or is it just Google continuing to refine the search experience? Stay tuned to find out. For now, we will continue to monitor the Video rich results every week to see how this trend progresses over time. 

Growth of Impressions for Recipe Rich Results

In August, 83% of our customers reported seeing the impressions for their Recipe rich results grow at an average of 3513% compared to July. 

This September, we are continuing to see an upward trend in the clicks and impressions for Recipe rich results with 60% of our customers seeing growth in impressions for their Recipe rich results. 

Google Updates in September 2022

More Product Search Features can be achieved with Product Schema Markup

On September 13, Google announced that merchants can now be eligible for merchant listing experiences simply by adding Product Schema Markup to their web pages, even if they do not have a Google Merchant Center account. 

This is awesome news! Now instead of just getting review ratings, price and availability, Product Schema Markup can help you achieve new experiences in search. With Google and Amazon battling for product search, we see this as Google’s move towards enabling more products to be shown on the SERP. 

Google currently has two broad categories of experiences:

1. Merchant listing experiences for web pages that allow shoppers to buy a product on the site.
   
   In the past, these merchant listing experiences (Shopping Knowledge panel, Popular products rich result, shopping experiences in Google Images and Google lens) were only open to users with Google Merchant Center accounts and the product details shown in these rich results were supplied from the Google Merchant Center feed.
2. Product snippets for a broader set of web pages with product information (including pages that sell products, publish product reviews, and/or aggregate information from other sites).
   
   In the past, these product snippets were powered by schema.org Product Schema Markup. This is now no longer the case.

The Google Merchant Center feeds are no longer necessary for merchants to be eligible for these enhanced Product search features. Google has added more Product Schema properties (i.e. colours, size, material, pattern, audience, hasEnergyConsumptionDetails, etc.), which allows merchants to add more product details to their Schema Markup and achieve the rich results that drive traffic to their site. 

To help you monitor and manage your Product rich results, Google has introduced two new Search Console Reports. 

1. Merchant listing report
   This is a new report that identifies Schema Markup issues for free listing experiences in the search results. Merchants that sell products on their online should use this report.
2. Product snippets report
   This report replaces the previous Product report with adjustments related to the separation of the merchant listing report. The data from the previous Product report is still available within this report. The report identifies Schema Markup issues for product snippets in the search results. Sites that don’t sell products online but still publish pages with Product Schema Markup should use this report.

September Core and Product Review Update

This month, Google rolled out their September Core Update (the second broad core update of the year) and the Product Reviews Update. Both updates came after the Helpful Content Update finished rolling out on September 9, 2022. We have yet to correlate the impact of these updates with the fluctuations in the search results. However, we will continue to monitor and report on any fluctuations we’ve observed.

Google using Structured Data for Machine Learning

Marie Haynes and Alan Kent from Google released a podcast episode on Product Reviews this September. 

In the episode, Alan Kent said “They look at behaviour to decide if the review is helpful. The machine learning then looks at patterns to see if the review is helpful.” 

This wasn’t the first mention of machine learning from Google in the past year. 

In the podcast, “Structured Data what’s it all about” on April 7, 2022, Ryan Levering from Google said, “Most of our features over time migrate to that approach where we ingest it. Maybe we start with one approach where we’re just using ML. And then we eventually add markups so people have control. Or it’s the opposite way around. And we start– we bootstrap with markup in an eco-system approach where people are giving us data. And then we enhance coverage of the feature by adding ML long run.” (Ryan Levering @ 8:34)

To do machine learning, Google needs to broaden its data sources. They now get data from their crawls, Merchant Center, user clicks, and Structured Data.

Conclusion

Google is making a lot of updates to the algorithm and evolving how they are using machine learning to delight the user in their search experience. As a result of these changes, SEOs need to diversify their rich result portfolio, stay agile and be ready to evolve their content and Schema Markup to capitalize on the changes.

Schema Markup (aka Structured Data) helps search engines understand your content because you’re translating it into machine language. With Google relying more on machine learning, Schema Markup will increasingly be of greater importance for sites to communicate with Google’s machines and maintain their visibility on the SERP.

If you haven’t already implemented Schema Markup as part of your SEO strategy, what are you waiting for? Get in touch with us today to learn how Schema App can help your organization stand out in search through Schema Markup.

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