But even with a long history, knowledge graphs are more relevant than they’ve ever been. According to Gartner’s Emerging Tech Impact Report, a robust knowledge graph is imperative for organizations looking to implement generative AI technologies. Knowledge graphs can help organizations ground their AI initiatives—like LLMs—in factual data about the organization.

If you’re interested in building a knowledge graph but are unsure where to start, you’re in luck. The good news is that if you have a website, you can construct a reusable content knowledge graph that supports both SEO and your internal AI initiatives.

This article will take you through the four steps of building a content knowledge graph using the Schema.org vocabulary.

Why should you use Schema.org to build your content knowledge graph?

You can create a knowledge graph using any number of ontologies, vocabularies, or glossaries. However, Schema.org should be the vocabulary of choice for constructing a content knowledge graph since it allows you to simultaneously maximize the SEO benefits.

Help search engines clearly understand and contextualize the content on your web page

The Schema.org vocabulary was created by major search engines as an industry-standard vocabulary for translating human-readable web content into a language that machines understand. By using this vocabulary to construct a knowledge graph based on your content, you’re also reaping the SEO benefits that come with it, including:

• Equipping search engines with an accurate understanding of your brand content
• Facilitating accurate and pertinent search queries that closely match your content
• Driving more targeted, engaged, and quality traffic to your site

Achieve rich results and stand out in search

By annotating your web content with the required Schema.org types and properties, search engines like Google may award visually enhanced search features for content like Products, Videos, Recipes, and Ratings. These rich results present key information directly in the SERP, and can increase click-through rates and drive more engagement and quality traffic to your pages.

Building Your Content Knowledge Graph

So you know about the SEO benefits of using Schema.org, but how does that get you a content knowledge graph? In the book Knowledge Graphs: Methodology, Tools and Selected Use Cases, Semantic Web and Knowledge Graph Experts, Fensel et al., break down the process of creating a knowledge graph into four steps:

1. Knowledge Creation,
2. Knowledge Hosting,
3. Knowledge Curation, and
4. Knowledge Deployment.

We’ve applied an SEO lens to these steps to explain how you can create a robust content knowledge graph using your organization’s web content. Let’s get started.

Step 1: Knowledge Creation

The first step to building a content knowledge graph is having high-quality, original content on your website and marking up that content using the Schema.org vocabulary.

Have high-quality content on your website

As a general first rule, you need to ensure your website content supports the specific objectives of your organization. Whether you’re selling good and services, educating the public, or wanting to build authority in a particular domain of expertise, the content across your website should exist to support those goals.

Beyond this, Google has shared guidelines on what it deems “helpful, reliable, people-first content.” This is an excellent resource that provides a series of questions you can use to assess the quality of your content. For example, you’ll want to ensure your content provides:

• Original information, reporting, research, or analysis
• A substantial, complete, or comprehensive description of the topic
• Substantial value when compared to other pages in search results

Marking up your content using the Schema.org vocabulary

To start building your content knowledge graph, you must annotate your high-quality content using types and properties from the Schema.org vocabulary. The annotations can be expressed in various formats, but Google recommends using JSON-LD. This translates the human-readable content on your website into machine-readable statements called RDF triples.

Include URIs in your Schema Markup to disambiguate your entities
In order to provide value beyond SEO, the entities in your Schema Markup must be represented by Uniform Resource Identifiers (URIs).

In JSON-LD, these identifiers appear as @ids to give the entities in your markup a unique identity that disambiguates and differentiates them from other entities – similar to how a social security number can uniquely differentiate people who may share the same name.

While Schema Markup still provides SEO value without including @ids, they are a requirement for the markup to become a reusable knowledge graph.

How to Apply JSON-LD to Web Pages
There are a few options for implementing Schema Markup on your web pages. You can manually author the JSON-LD and insert it in your webpages’ HTML, or you can use a plugin to generate and deploy the markup on your site.

Manual authoring requires technical expertise and isn’t scalable if you have a large number of webpages, and while plugins that auto-generate markup are a scalable authoring solution, the markup is generally much less descriptive. If you want to customize your markup and ensure it is dynamic and connected, we recommend using the Schema App Highlighter to generate and deploy your markup at scale without having to do any manual coding.

Whatever method you choose, the Schema Markup you author must appear in the HTML of the webpages being described, making it available for search engines and other web crawlers. In this state, your webpage content transforms into semantically enriched data, but this data doesn’t truly become a knowledge graph until it’s been collected and stored.

Step 2: Knowledge Hosting

In the hosting step, the Schema Markup you’ve authored for your website must be collected and hosted in a way that allows the RDF data to be retrieved.

Collecting the Schema Markup

There are two ways of collecting the Schema Markup once it has been applied to a website:

1. Crawling: Where a crawler crawls a website, extracts the JSON-LD that has been applied, and stores it in a knowledge graph.

2. Mapping: Many tools that map content to Schema.org will also store that markup in a knowledge graph.

But where does this storage occur?

Storing Data

Because knowledge graphs are represented as RDF triples, the best place to store them for easy retrieval is an RDF database or triplestore. There are a variety of RDF stores available. Examples include:

• OpenLink Virtuoso
• Ontotext GraphDB
• Amazon Neptune
• Stardog
• AllegroGraph

For more information and to compare the various options, check out DB-engines.com. They rank the popularity of database management systems and provide helpful analysis.

Retrieving Data

You can retrieve RDF data from a database or triplestore using SPARQL – an RDF query language. In the simplest terms, SPARQL uses known information to find unknown information (variables) using pattern matching.

For example, we could write a SPARQL query that says, “Find all the people in my database who work for Schema App and know about semantic technology.” “Mark van Berkel,” our co-founder, would return as a match, and so would all other entities in our knowledge graph that match the same criteria.

When you add Schema Markup to your website using Schema App’s authoring tools, we host that data for you in our Knowledge Graph Data Platform. You can query your own graph using the SPARQL endpoint interface in your account. You can also use our Export Data API to export your knowledge graph for reuse in other contexts.

Once you have found an appropriate way to host your knowledge graph, you can move on to curation.

Step 3: Knowledge Curation

It is a well-known fact that cleaning data is time consuming, and resource intensive, especially if you’ve got a lot of it. That said, we will address 3 of the most important aspects of curating your data to ensure your high-quality web content has resulted in a high-quality content knowledge graph.

In the knowledge curation step, you should ensure that the data within your content knowledge graph is:

• Accessible
• Correct
• Complete

Let’s break those down further.

Accessible

The data in your knowledge graph needs to be available.

For example, when extracting your content knowledge graph from your website, you’ll want to ensure that none of your web pages run into issues like “404 not found” errors. You will also want to ensure that the RDF store you’ve selected for hosting keeps your data retrievable and secure.

Correct

Your markup is free of syntax errors
The language used to express your knowledge graph can’t have syntax errors like missing commas or brackets in the wrong places. Auto-generated markup from plugins or other authoring tools will prevent this from happening, but if you’ve authored your markup manually, you’ll need to take extra precautions. Syntax errors can be identified on a page-by-page basis by running your pages through the Schema Validator.

The markup must align with the content on the page
If you make content changes to your page without updating your markup, your knowledge graph will become out of date.

Assessing whether the statements in your markup are correct and up-to-date can be difficult depending on the size of your dataset and how you manage your Schema Markup. This is especially true if you implement your markup manually. As previously mentioned, data cleanup is complex and resource-intensive, and becomes ever more so as your content grows and changes over time.

Therefore, we recommend using a dynamic Schema Markup generator tool like the Schema App Highlighter to ensure your page’s markup always aligns with its content and your RDF triples remain correct.

The markup follows the Schema.org vocabulary guidelines
You also need to ensure that your entity types use the most descriptive properties and that the properties used connect to expected types. For example, I can’t say that a Person worksFor another Person, because Schema.org states that the worksFor property can only connect a Person to an Organization.

The types and relationships you apply to your data dictate what you’re able to query for in your graph, and as a result also play an essential role in the completeness of your Knowledge Curation.

Complete

Ensure your knowledge graph contains enough data to answer queries relevant to your use cases. For example, if you want to know the correlation between ratings for products of specific sizes, colors, or prices, those properties must exist in your data.

In cases where your content references well-known entities (like brands, people, places, or concepts), you may also want to implement entity linking. Entity linking is a process that identifies entities in text and links them to corresponding known entities from external knowledge bases like Wikipedia, DBpedia, and Google’s Knowledge Graph.

You can apply entity linking:

1. Manually for absolute precision
2. Automatically using Natural Language Processing APIs

Once embedded in your markup, these entities provide additional SEO value by helping search engines like Google disambiguate and contextualize your content to provide more accurate results for search queries. When it comes to your content knowledge graph, entity linking makes your knowledge graph more descriptive, providing an even richer data layer for you to reuse.

Step 4: Knowledge Deployment

The knowledge deployment stage transforms the knowledge graph’s theoretical structure into practical applications that drive tangible benefits for your organization and its stakeholders. In fact, I prefer to call this the “Reuse” stage, since this is when you can finally reuse the knowledge graph you’ve created for all sorts of different initiatives.

To reap the SEO benefits we’ve previously described, you’ll need to ensure you’ve published your Schema Markup externally for search engines to consume.

Beyond the SEO benefits, your content knowledge graph can be reused for things like enhancing user experience, content optimization, and AI and machine learning. Let’s explore these opportunities further.

Enhancing User Experience

You can utilize your content knowledge graph to improve website navigation and internal search functionality.

For example, if a user visits a product page on an eCommerce site for smartphones, the content knowledge graph can be leveraged for a recommendation engine to dynamically generate suggestions based on the products being viewed. This can appear as a “You May Also Like” section or complementary products, like phone cases or chargers, suggested during checkout. This enhanced user experience can significantly increase engagement and conversion rates.

Content Optimization

You can use your content knowledge graph to optimize existing content or identify gaps in your content.

For instance, your organization likely publishes blog posts on various topics. With a content knowledge graph, you can analyze the connections among entities in your blog posts. This analysis helps you pinpoint clusters of related topics or categories that have more coverage. If you notice gaps in the topics your organization wants to emphasize in their web presence, you can create additional content to fill those gaps.

AI and Machine Learning Applications

Organizations can use knowledge graphs to accelerate their AI initiatives, including Chatbots and other LLM functions.

Knowledge graphs provide a foundation for training AI and machine learning models for tasks such as natural language processing, recommendation systems, and predictive analytics. Knowledge graph data is already structured, making it easier for machines to process than unstructured content (natural language). This makes using AI less costly as use continues to scale.

If you’re concerned about the risks of hallucinations from LLMs, you’ll be happy to know that knowledge graphs can also be leveraged for Retrieval-Augmented Generation (RAG), resulting in more accurate answers to queries.

These are just some of the ways a content knowledge graph can support your organization in this rapidly changing technical landscape. And the best part is, you can easily construct one with the pre-existing data that constitutes your website.

Developing a Content Knowledge Graph for Your Organization

Although creating a content knowledge graph has only four steps, implementing these steps can be resource-intensive. However, with the numerous possibilities for reuse, building a content knowledge graph is a worthwhile investment that will yield a strong return as semantic search, AI, and knowledge management continue to evolve.

At Schema App, we can help you implement your Schema Markup data layer and develop a semantically enriched reusable content knowledge graph to prepare your organization for AI and support your semantic SEO efforts.

Get in touch with our team to learn more.

The post The 4 Steps to Building a Content Knowledge Graph appeared first on Schema App Solutions.

While we remain uncertain about the impact of these new experiences on search engine optimization, there are a few things that we know:

• Google’s Search Generative Experience (SEG) will feature source links.
• Generative AI is in its infancy, but it is not going away.
• AI uses large language models to train. Large language models use Knowledge Graphs to learn and ground their data to increase accuracy.
• Schema Markup when done in a connected way, creates a knowledge graph that AI can consume.

Organizations should use Schema Markup (also known as Structured Data) on their websites if they want to be prepared and thrive in AI search.

The last point is especially important because AI is going to need help understanding and contextualizing the content on a website to provide searchers with the right answers.

Before exploring deeper into the threats and opportunities of this new technology, let’s talk about generative AI in the context of search.

What Is Generative AI for Search?

Generative AI is a specific type of artificial intelligence algorithm that is designed to generate new content and outputs based on data they have been trained on. Some of the content that generative AI can create includes text, audio, code, and images.

Bard and ChatGPT are prime examples of generative AI chatbots that have been trained on large quantities of unlabeled, unstructured data. While generative AI chatbots are a groundbreaking development in the world of artificial intelligence, they are only the beginning.

During Google I/O 2023, the search engine giant showcased how it has incorporated generative AI technologies into its search engine and other products such as Workspace. This newly integrated AI technology will have a significant impact on Google’s responses to queries and the composition of the search engine results page (SERP).

There is considerable excitement surrounding the AI revolution. That said, there are also significant concerns around bias, plagiarism, trustworthiness, and accuracy of generative AI search results.

Currently, generative AI search engines are largely trained on unlabeled/unstructured data which can lead to inaccurate results. Adding structured data or Schema Markup to your site can allow search engines to:

• train on quality data,
• improve the accuracy of their answers,
• and give you a control point to inform generative AI on your web content.

There are underlying risks and exciting opportunities associated with the implementation of any new technology, especially when that technology is as powerful and sophisticated as generative AI.

Google’s new Search Generative Experience will have an impact on the way SEO is done. So is generative AI a threat or an opportunity for search engine optimization?

Let’s explore some of the potential threats and clear opportunities that have emerged in the wake of generative AI search.

Is AI Search an Opportunity or Threat for SEOs?

At first glance, Google’s new generative AI search has a few glaring threats to SEO.

Threats

1. Potential Loss of Visibility on the SERP

There are a lot of unanswered questions about how website performance will be measured with AI search.

Google’s generative AI search engine does showcase links that attribute information back to reputable source content. However, users may no longer need to click to read this content (resulting in zero-click searches), which limits your visibility and overall influence over the customer journey.

Ultimately, rich results, featured snippets and other top-ranked content may be overshadowed by generative AI results. In turn, this could lead to a reduction in rankings, clicks, and impressions.

2. Success Metrics of a Website Are Going to Fundamentally Change

Generative AI has the potential to condense your sales funnel. The line between clicks, impressions, and conversions could become blurred, making it difficult to attribute sales or leads to specific marketing activities.

Bing and Google have yet to reveal how sites can measure their performance in generative AI search results. For now, you will have to draw conclusions using existing metrics.

3. Hyper Long and Specific Search Queries

If you haven’t watched the Google I/O segment on search yet, we strongly recommend doing so. In their demonstration, we saw that the Search Generative Experience (SGE) allows for more natural, specific conversational search queries in comparison to the traditional keywords or questions we see today.

Image Credit: Google

This capability will lead users to conduct very specific queries. And as a result, question fragments and traditional keyword searches will become less prominent.

These long specific queries are a shift from traditional keywords and will likely have little to no search volume. It also reflects SGE’s ability to understand the context behind a query rather than the keywords used. Therefore, it is crucial for content publishers to focus on utilizing Schema Markup helping search engines to understand and contextualize the on-page content.

Furthermore, Google has previously emphasized the importance of a website focusing on content around a specific topic as a way of creating people-first content.

Instead of focusing heavily on optimizing for keywords, focus on creating content for topics within your expertise and around your customers’ needs. You can then use Schema Markup to communicate your expertise, experience, authority and trustworthiness with the AI search engine.

Opportunities

The world of SEO is constantly evolving. Long-time marketing professionals understand that this level of change is not a new phenomenon. To stay relevant in the world of organic search and differentiate yourself from the competition, you must adapt to the latest trends (such as generative AI) and capitalize on the opportunities they provide.

The key opportunities created by generative AI search include the following:

1. Provide a Better Customer Experience

You can provide customers with a better experience by answering their queries directly on the search engine results page. Today, content publishers can already utilize rich results such as FAQ and How-To as well as other features like Featured snippets to answer the questions in search.

However, the generative AI search engine can help you reduce friction with your customers by showing users the key information they need to know or providing follow-up information.

The eCommerce industry is one of the first industries to be truly disrupted by this because Google already has great eCommerce data from Google’s Shopping Graph for their AI search engine to train on.

Image credit: Google

Generative AI can shorten the consumer funnel by allowing searchers to convert immediately through the SERP and purchase an item directly from the SERP instead of having to navigate the site. This will mean that conversion data and source-to-conversion pages will be one of the key metrics during this evolution.

2. Links Are Not Going Away

Based on the demo and the responses from the early experimental rollout of Search Generative Experience, Google’s generative AI search engine is linking back to sources more frequently than initially expected. In fact, Google’s SGE demo gave a lot of real estate to the websites where the content was originally sourced from.

Though far from perfect, this is an opportunity for search engine optimizers to capitalize. We believe that content publishers can focus on optimizing their website for search by following Google’s guidelines of creating people-first content and E-E-A-T, optimizing on-page experiences for your audience, and monitoring or optimizing core web vitals.

It doesn’t matter whether you are new to search or simply want to stay ahead of the pack. You should refine all these key SEO areas to gain an edge over the competition.

3. Schema Markup Is Key to Helping AI Search Engines Understand Your Content

In a keynote address at Pubcon 2023, Fabrice Canel from Bing expressed that annotating great content with Schema Markup is how search engine optimization experts should prepare for generative AI search.

Thus, you should craft compelling, people-first content before implementing Schema Markup across all web pages. Schema Markup helps search engines understand the content on your site so it can provide users with useful results.

While many SEO teams already use Structured Data to achieve rich results, few implement proper semantic Schema Markup across their entire site. To help generative AI search engines truly understand and contextualize your content, you need to ensure your Schema Markup is connected to other entities on your site and other external authoritative sources.

Implementing connected Schema Markup can help you develop your knowledge graph that generative AI search engines can then utilize to infer new knowledge.

At Schema App, we are a semantic technology company. Since we started doing Schema Markup at scale in 2016, we’ve focused on implementing proper, connected Schema Markup across our customers’ sites to help search engines fully understand and contextualize their content.

If you are looking to optimize your website for AI search using Schema Markup, get in touch with our team for more information.

4. Focus on Content Quality

Over the previous year, Google launched a helpful content system intended to ensure that content is “people-first” instead of “search-engine first.” This emphasis on content made by people, for people, demonstrates Google’s commitment to providing users with relevant results that address their pain points. These changes are aligned with the type of content that Google SGE and the new Bing experience are highlighting.

Generative AI is still in its early stages and the accuracy of their answers is still questionable. In light of this, there is an opportunity for content publishers to create high-quality, trustworthy content for Google’s generative AI search engine to train on.

You should also ensure that your content is created by human writers and that each piece of copy addresses specific topics relevant to your audience.

SEO Is Changing

The bottom line is that the SEO landscape is evolving quickly. Google’s Search Generative Experience is still in its experimental phase and we don’t know its full impact on SEO or the SERP.

While it is unclear what these changes will mean for your SEO strategy, now is the time to start preparing for this change. In a world where many circumstances are out of your control, you should focus on the factors that you can manage – such as the quality of the Schema Markup on your website.

Schema Markup is one of the SEO tactics you can implement immediately to prepare for AI Search and Schema App can help your business implement semantic Schema Markup using our end-to-end solution. Get in touch with our team to learn more about how we can help you prepare for generative AI search.

The post Is Google’s Generative AI Search an Opportunity or Threat for SEOs? appeared first on Schema App Solutions.

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.