As a result, users and search engines are increasingly focused on accessing high-quality, reliable information. This shift has led many organizations to revise their content strategies to maintain accuracy, relevance, and trust in this evolving environment.

To develop a successful content strategy in this new search experience, marketers must address several critical questions on an ongoing basis:

• How can you maintain an up-to-date content inventory?
• What content gaps exist, and where are opportunities for new high-quality content to be added?
• Which existing content pieces require improvement or disambiguation?
• How has your content impacted your website’s performance as search evolves?

While your website content is a rich data source, it’s often unstructured. This makes it difficult to analyze and answer these questions at scale. Many marketers manually review and revise content to inform their strategy, which is time-consuming and inefficient.

So what if you could automatically structure your content to make high-level analysis fast and easy? Good news, a content knowledge graph can be leveraged to do precisely that. This approach is particularly valuable for large websites or organizations managing multiple sites, where understanding the full scope of covered topics can be challenging.

This article will explore how leveraging your content knowledge graph can support and enhance your content strategy.

By harnessing the power of your content knowledge graph, you can make well-informed decisions that drive your content strategy forward in today’s competitive digital landscape.

Understanding Content Knowledge Graphs

At Schema App, we define a content knowledge graph as a graph that represents entities (things), their attributes, and the relationships between them on a publicly-facing website.

Like a general knowledge graph, it uses a standardized vocabulary or ontology (such as Schema.org) to create a structured, reusable data layer. This structure enables machines to discover new insights through inferencing, helping to explore and understand the connections between various entities in your content.

At Schema App, we build content knowledge graphs by mapping the content on your website to specific types and properties in the Schema.org vocabulary. This results in a precise and organized framework that accurately reflects your content’s meaning and relationships.

While Schema.org provides an excellent foundation for knowledge graph creation, its available types and properties can be limiting. That’s why Schema App created the Omni Linked Entity Recognition (Omni LER) feature, which automatically identifies entities in your content that have been described in external authoritative databases such as Wikipedia, Wikidata, and Google’s Knowledge Graph. This process is known as entity linking, and it offers two significant benefits:

1. Improved SEO: Embedding these entities in the Schema Markup on your pages helps to disambiguate them, which enhances search engine optimization for queries related to those entities.
2. Content Inventory: Identified entities also function as an inventory of what’s discussed in your content, offering valuable insights for content strategy planning.

By leveraging both the Schema.org vocabulary and Omni LER, the content knowledge graph provided by Schema App gives you a comprehensive understanding of your content architecture. This enables you to make data-driven decisions to optimize your content strategy for search.

Content Knowledge Graph Use Cases

Now that you’ve been introduced to content knowledge graphs, let’s explore some practical applications and effective ways to leverage this powerful resource to enhance your content strategy.

Improve Content Inventory Organization

When you develop a content knowledge graph with Schema App, you can implement a multi-dimensional categorization method for your content.

Schema App’s Highlighter builds your content knowledge graph by consistently tagging and classifying your website content at scale. This is particularly beneficial for organizations with large websites, a wide variety of assets, and different content stakeholders.

Your content knowledge graph establishes meaningful connections between different content pieces based on entities, types, and properties – not just keywords. For example, a blog post would likely show up in your content knowledge graph as an instance of a BlogPosting with properties like author, datePublished, and dateModified. If Omni LER is also used, additional metadata about the identified entities mentioned within the article body will be added. This enables you to do more detailed content analysis, which we will cover later in the article.

Content Coverage and Gap Identification

By constructing your content knowledge graph with both the Schema.org vocabulary and Omni LER, you can query all of your content with greater precision. Schema.org provides detailed types and properties, and Omni LER adds unique entities for varied levels of granularity in your data layer. When combined, you can leverage your content knowledge graph to help you determine what new content to add or which existing content to improve to better meet your audience’s needs.

This holistic view of the Schema.org types and entities covered by your website allows you to:

• Identify areas of content saturation
• Discover underrepresented topics
• Align your content with current business goals and market trends

Use Case 1: Aligning Content With Business Goals

For instance, one of Schema App’s customers aimed to be recognized for their product’s ease of use. To align their content with this business goal, we employed the following strategy:

1. First, we identified Schema.org types and properties that indicate user support and ease of use. These included:

• HowTo
• BreadcrumbList
• FAQPage
• VideoObject

These content types can represent ease of use by making processes feel manageable and easy to follow, empowering users to self-serve, or simplifying site navigation.

2. We then queried their content knowledge graph to pinpoint where these types and properties already exist on their site and where existing content could be further enhanced to align content with their goals.

3. Finally, we identified opportunities to add net new content in alignment with their goal. For instance, we recommended creating more HowTo content with clear steps and accompanying images and/or videos to support ease of use.

Through this process, our customer identified content gaps that, when addressed, aligned better with their business goals and enhanced the quality of their site’s content.

Just like how content knowledge graphs can identify gaps in your content, they can also reveal how much of your existing content overlaps with desired entities. This information is crucial for ensuring that your content strategy covers all necessary areas and effectively addresses your audience’s interests.

Use Case 2: Assessing and Revising Content Coverage

Consider another example from one of our healthcare customers:

Our customer did entity linking using our OmniLER feature. The feature automatically identified known entities in their content, which revealed an unexpected insight: they had numerous blog posts mentioning COVID-19, a topic they no longer wished to emphasize in their content strategy.

Armed with this information, the customer was able to:

• Quickly identify all content pieces mentioning COVID-19
• Assess the relevance and necessity of each mention
• Selectively remove or update content to align with their updated business goals

This targeted approach allowed the customer to refine their content strategy without needing a time-consuming manual review of their entire content inventory.

Disambiguating Entities to Ensure Brand Name Consistency

Your content knowledge graph can also ensure the disambiguation of your entities and brand voice consistency across your website. This capability is particularly valuable when dealing with ambiguous terms or acronyms that could lead to misinterpretation or unintended associations.

For instance, imagine a scenario where our brand, Schema App, faces a challenge when its name is shortened to just “Schema” in some content. The word “Schema” can refer to various concepts on the web, from psychology to structured data. If machines unintentionally link this shortened form to unrelated information, it could confuse and potentially damage our brand image.

To resolve this issue, we would leverage our content knowledge graph to:

• Locate all instances where our brand name is inconsistently represented
• Implement a standardized approach to always use our full brand name, “Schema App”
• Disambiguate our brand using Schema Markup and entity linking. This ensures our brand is accurately identified and associated with the correct definition in external authoritative knowledge bases
• Ensure that our brand is consistently and correctly represented across all content

This scenario illustrates how a content knowledge graph enables organizations like ours to:

• Gain a holistic view of entity usage across our content
• Identify areas where content should be more explicit based on entity interpretation and their links to external knowledge bases
• Make informed decisions about content revisions to maintain brand integrity
• Ensure consistent brand voice and messaging across all content

By leveraging a content knowledge graph, we can proactively address potential ambiguities, maintain brand consistency, and enhance our content’s overall quality and clarity. This approach not only improves user experience but also protects our brand from unintended associations or misrepresentations, ultimately enhancing our performance in search.

Schema App Helps Develop Your Content Knowledge Graph

As explored throughout this article, a content knowledge graph is a powerful tool for optimizing your content strategy, improving SEO performance, and preparing your organization for the future of AI-driven search.

At Schema App, we implement semantic Schema Markup and automate the entity linking on your website to develop your organization’s content knowledge graph.

If you’re a current Schema App customer interested in leveraging your content knowledge graph, we encourage you to reach out to your Customer Success Manager. They can take you through what’s currently available so that you can leverage your content knowledge graph to support and enhance your content strategy.

If you’re new to Schema App and interested in harnessing the power of a content knowledge graph for your organization, now is the perfect time to get started. Our team is ready to help you navigate the complexities of semantic SEO and knowledge graph development, ensuring your content strategy is primed for success in today’s digital landscape.

Don’t let your content strategy fall behind in the era of semantic search and AI. Contact our team today to begin developing your content knowledge graph and optimizing your content strategy for search.

The post How to Optimize Your Content Strategy Using Your Content Knowledge Graph appeared first on Schema App Solutions.

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.

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.

A knowledge graph is a structured representation of knowledge that describes entities and the relationships between them.

Knowledge graphs are a part of “knowledge representation“, a field of Artificial Intelligence (AI) that deals with presenting data in a way that enables machines to engage in reasoning, problem-solving, decision-making, and inferencing.

The versatility of knowledge graphs extends across various domains, with use cases that include:

• Recommendation engines
• Targeted advertising
• Data analysis
• Semantic search
• The training of Large Language Models (LLMs)

Knowledge graphs empower machines to extract meaningful knowledge from data by presenting information in a machine-readable format.

But did you know you can also create a “content” knowledge graph that is particularly useful for SEO initiatives? Although structured like a general knowledge graph, a content knowledge graph functions as a representation of the content on your website.

This graph can be published externally for search engines to consume, be employed for internal AI projects, or be used to identify content gaps.

Moreover, these graphs establish a robust foundation for developing more extensive marketing knowledge graphs if you have additional data sources you’d like to bring into the fold.

But before we get into that, this article will explore the basic components of a knowledge graph to enable you to develop your own content knowledge graph using the content on your website.

Anatomy of a Content Knowledge Graph

At its simplest form, a knowledge graph fundamentally consists of nodes and edges.

Nodes represent entities within a knowledge graph, and edges interconnect these nodes, delineating the relationships between them.

To fully understand how a knowledge graph works, it’s important to know the technologies required to build them.

Our focus in this section is to guide you through the key terminology and functions that are critical to the development of a robust content knowledge graph.

Uniform Resource Identifier (URI)

In the realm of knowledge graphs, the Uniform Resource Identifier (URI) plays a crucial role in uniquely identifying entities. A URI is a distinctive string of characters designed to distinguish and disambiguate a specific resource on the web.

Similar to license plates on cars that enable individual identification despite many people sharing the same make and model, URIs serve a similar function by ensuring the unique identification of various resources amidst the vast expanse of the internet.

At Schema App, we generate HTTPS URIs for entities defined in your Schema Markup, as shown in the image below. These URIs appear in the @id attribute. They allow you to link the entities on your site within your markup and enable search engines to identify the entities in your knowledge graph.

This systematic identification enables efficient communication and access to resources across different platforms and technologies. Within the context of a knowledge graph, URIs represent entities.

Entities

An entity, as defined by Google, denotes a single, unique, well-defined, and distinguishable thing or idea. It possesses defining characteristics or attributes such as size, color, and duration. However, an entity’s true significance emerges when it is described in relation to other entities, giving it contextual meaning.

This is where RDF Triples play a pivotal role, providing the framework to represent these interconnected relationships between entities within a knowledge graph. But first, what is RDF?

RDF

RDF, which stands for Resource Description Framework, is a standardized method for expressing data in the form of a directed graph using subject-predicate-object statements, commonly referred to as “triples.”

RDF Triples

The foundational unit of a knowledge graph is the triple. It comprises two nodes that represent entities connected by a single edge to articulate their relationship. Represented as “subject-predicate-object” statements, a triple illustrates how an entity (subject) links to another entity or a simple value (object) through a specific property (predicate).

As these triples combine, they form interconnected graphs of resources, laying the groundwork for a comprehensive knowledge graph. However, to provide meaning to the machine, you must express these triples in a machine-readable format.

You can express RDF triples in a variety of formats, including:

• Turtle
• RDF/XML
• And JSON-LD

The most widely adopted format is JSON-LD, which we utilize here at Schema App.

JSON-LD

JSON-LD, or JSON for Linked Data, is a serialization format for expressing RDF triples. It is relatively easy for humans to read and write and also for machines to consume. It is also the preferred Schema Markup format for search engines like Google.

JSON-LD code allows machines to understand RDF statements about entities.

For example, Mark van Berkel is an author for the Schema App blog, and his author page states that he works for the organization Schema App. On the left is the Schema Markup expressed in JSON-LD telling machines that Mark van Berkel (Person) works for Schema App (Organization). On the right is this same code visualized as an RDF triple, depicting these same entities and illustrating the relationships between them.

Ontologies

The last component in a knowledge graph is an ontology.

In Information Science, an ontology is a “formal, explicit specification of a shared conceptualization,” essentially serving as a blueprint for defining what exists in a data model (i.e. the method for describing contents within a database).

This model typically encompasses three key elements.

First, we have classes, also known as types, representing categories of entities such as an organization, event, or person.

Secondly, attributes, aka properties, are used to describe an entity. For instance, a Person entity might possess a name as one of its attributes.

Lastly, relationships, which are also represented by properties, delineate how one entity connects to another. These are similar to attributes in that they describe an entity, but more specifically, they describe how one entity connects to another entity.

For example, a Person may have a parent, child, or colleague relationship with another Person who will have their own attributes.

A wide variety of ontologies, vocabularies, and glossaries exist for categorizing and relating data, with Schema.org standing out as one of the most widely used in SEO. While technically a vocabulary and not a strict ontology, Schema.org effectively fulfills the role of describing categories of things and the relationships between them.

Building a Content Knowledge Graph with Schema.org

Founded in 2011 by Google, Bing, Yahoo, and Yandex, Schema.org emerged as a collaborative effort to enhance the web by introducing a standardized vocabulary. This initiative aimed to transform human language into a structured, machine-readable language.

All major search engines would support this language, improving their ability to match search queries with relevant results, making it beneficial for SEO purposes.

While SEO strategies commonly employ Schema.org, its utility extends beyond; it can also serve as a robust tool for constructing a knowledge graph.

Leveraging the Schema.org vocabulary allows you to organize your website content into a graph of interconnected entities. To achieve this, you can utilize the types and properties defined by Schema.org to express RDF triples in a machine-readable format like JSON-LD, all while representing your entities with URIs.

See how all of these terms come together?

This amalgamation of elements effectively creates a content knowledge graph for your organization.

Construct a Content Knowledge Graph for Your Organization

Developing your own content knowledge graph is essential for optimizing your semantic SEO strategy. It readies your content for the future of search and drives higher-quality traffic to your site.

Knowledge graphs empower search engines to infer knowledge through additional contextual information, bridging gaps for more relevant results. As such, this deeper comprehension should drive more qualified traffic to your site and boost the CTR for relevant pages.

At Schema App, we specialize in building and managing content knowledge graphs through the use of Schema Markup. Our dynamic authoring solutions ensure your Schema Markup is always descriptive, interconnected, and up-to-date.

Whether you’re integrating Schema Markup into your SEO strategy or aspiring to transform your content into a reusable data layer, Schema App has you covered.

Interested in building a content knowledge graph for your own organization but aren’t sure where to start? Schema App handles the technical aspects, enabling you to reap the benefits of having a well-constructed content knowledge graph without imposing the technical burden on your internal teams.

Contact our team today to get started.

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According to Google, an entity refers to a single, unique, well-defined, and distinguishable thing or idea.

Entities can be diverse, ranging from tangible elements like people, organizations, and products to abstract concepts and creative works. They possess defining characteristics or attributes, like size, colour, and duration. And most importantly, entities exist in relation to other things/entities.

Take, for example, “xylopental”. This is a string of characters that have no meaning to humans and, therefore, have no meaning to machines. However, if I invented a new musical instrument named “Xylopental,” this string of letters would become an identifiable entity. It is understood in relation to musical instruments, which is also an entity.

Entities need to be described in relation to other entities to have any meaning.

In its information architecture, Google often refers to entities as “topics.” From a content perspective, we can consider entities in SEO as topics within your content that become well-defined by referencing other related things.

Entities and their connections are crucial in developing Google’s Knowledge Graph. Google’s Knowledge Graph is a database that Google uses to quickly retrieve information about specific topics or entities. Any information Google has on a particular entity will show up in the Knowledge Panel, as shown below.

For example, when we search for “Berkshire Hathaway” on Google, we get a knowledge panel that conveys information about Berkshire Hathaway’s owner, stock prices, revenue, and more.

In the “People also ask” section, we can see queries that don’t specifically name Berkshire Hathaway, like “Does Buffett own McDonald’s?”

As the long-time owner and CEO of Berkshire Hathaway, Warren Buffett is often synonymous with his brand. McDonald’s is Buffett’s favorite breakfast meal, and he had previously purchased 4.3% of McDonald’s stocks but sold it in 1999.

This explains the inclusion of the question “Does Buffett own McDonald’s?” even though it doesn’t mention Berkshire Hathaway at all. All this information is derived through context from entities that are related to one another.

Difference Between Entities and Keywords

A common misconception SEOs have is that entities are just like keywords. Keywords are words or phrases that searchers use in their search queries. It can be a single word, a phrase, a sentence or a question. Historically, search engines would rank pages on the SERP using keyword matching.

However, the method of lexical search presented a few challenges.

1. Keywords tend to be ambiguous because certain words can have multiple meanings. For example, the word ‘Java’ can refer to either the programming language or the island of Indonesia.
2. Different languages tend to phrase the same things differently. For example, the term ‘rebord de fenêtre’ in French translates directly to ‘edge of window’ in English. But it is actually referring to a windowsill.

As a result, the old search algorithms were producing less relevant and accurate results for searchers.

Entities, on the other hand, are universally understood concepts that are not bounded by language or ambiguity. They are broader topics that keywords can stem from. They are distinguishable, especially through their relation to other things. Unlike keywords, entities have an additional layer of context, which can provide greater clarity to search engines.

How do Entities Relate to SEO?

Search engines are evolving toward a more semantic approach, analyzing the concepts and meanings within user queries. They identify relevant pages that answer the entities in question with greater context and accuracy.

As search engines advance in their understanding, there is inevitable demand for SEO strategies to also become more semantic to better align with this sophisticated and nuanced way of search. The good news is that you can assist search engines in grasping the entities and context of the content on your site.

Your website serves as the information hub about things related to your organization. The services provided by your organization, your postal address, your customer reviews, your blog articles – these are all entities related to your organization.

However, the content often exists in the form of plain text, images, videos and infographics. Humans can consume this form of information but machines and search engines cannot comprehend information in this unstructured manner.

Creating Machine-Readable Content

To bridge this gap between human understanding and machine interpretation, implementing semantic Schema Markup to define, describe and connect your entities is crucial. By meticulously defining entities within your content, you are essentially structuring your data in a format that search engines and machines can understand.

You can also further define the entities on your site by linking them to other linked entities in external authoritative databases like Google’s Knowledge Graph, Wikipedia, or Wikidata. This helps search engines disambiguate the entities on your page.

Defining these entities ensures your content is contextually understood by machines. This contextual understanding allows search engines to display your content for a broader range of relevant queries, expanding your site’s visibility and attracting a more qualified audience.

If you leave AI search engines to their own devices without informing them about the entities on your site, you are leaving it to them to decide on what is “true” for your content. You can control how machines interpret your content by defining your entities to prevent hallucinations and inaccuracies from being presented about your organization. This strategic approach safeguards your organization’s E-E-A-T and credibility.

So, now you know why you should define your entities, but how do you do it?

How to Identify and Define Page Entities

Author and Deploy Schema Markup

To have your content topics recognized as entities by search engines, use the Schema.org vocabulary to structure your data. You can use the Schema.org Types and properties to describe the entities across your site.

Many organizations tend to use a Schema Markup plugin to automate their Schema Markup process. However, many of these plugins will only markup certain page Types or properties. As such, you cannot customize your markup to properly define your entities or link them to other entities on your site.

If you want to provide search engines with a clear understanding of your content, you need to describe your entities thoroughly and leverage as many relevant properties as possible. The Schema App Editor and Highlighter are two great options if you want to implement custom semantic Schema Markup on your site.

Add Unique Identifiers to Schema Markup

For your entity to be identifiable and retrievable, it must have a distinct Uniform Resource Identifier (URI). URIs can help machines identify unique resources (like entities) and enable data interlinking.

In JSON-LD, this is expressed with the ‘@id’ attribute. By adding the ‘@id’ attribute to the entities in your Schema Markup, you can easily connect and refer back to other entities on your site so that search engines can clearly understand the relationship between different entities on your site.

For example, the author page for Mark van Berkel contains all the information about the person Mark van Berkel. Therefore, we can use Person markup on that page and define the entity ‘Mark van Berkel’ using the Schema.org properties. When we create the markup, we can add an ‘@id’ so that any connections to Mark can be indicated using the @id.

Search engines like Google can still read and qualify your page for a rich result if you don’t include an @id for your entities. However, you wouldn’t be able to connect the entities on your site in a machine-readable manner.

When you publish your Schema Markup using the Schema App Highlighter or Editor, our tool automatically generates HTTPs URIs for the entities defined in your Schema Markup.

Connect Your Entities

Connecting these entities on your website to explain how they are related, and extending these connections to external knowledge graphs, such as Google’s Knowledge Graph, Wikipedia, or Wikidata, helps search engines to disambiguate the entities on your site.

For example, Mark is one of the founders of the organization Schema App. We can leverage the ‘founder’ property under the Organization type to express that Mark is the founder of Schema App. And since we’ve already defined the entity Mark on his author page, we can link the entity ‘Mark’ using his @id to the entity ‘Schema App’ in the Organization markup.

That way, search engines know that this specific entity, Mark van Berkel, which is described on this page (https://www.schemaapp.com/author/vberkel/#Person), is the founder of Schema App.

As mentioned earlier, you can also connect your entities to external knowledge graphs to distinguish the entities on your site. External knowledge graphs are authoritative databases comprising millions of entities and their relationships. These entities link to other entities across the web which is why they are referred to as “linked entities”.

The linked entities identified in these external knowledge graphs also have unique identifiers, enabling connections to your own entities.

For example, Vancouver is the name of a city in British Columbia, Canada and also the name of a city in Washington State, US.

If your organization is a restaurant based in Vancouver, BC, you can describe your organization’s areaServed property by linking it to the right entity on:

• Google’s knowledge graph (/m/080h2), OR
• Wikipedia (https://en.wikipedia.org/wiki/Vancouver), OR
• Wikidata (https://www.wikidata.org/wiki/Q24639).

That way, search engines can clearly understand which Vancouver you’re referring to.

By establishing these relationships, you empower machines not only to comprehend existing information deeply but also to infer new knowledge based on this contextual understanding.

How do Entities Relate to Knowledge Graphs?

This process of defining and connecting entities effectively constructs a robust knowledge graph for your organization, providing a comprehensive and accurate representation of your content from a digital scope. Entities serve as the foundational building blocks of information that knowledge graphs organize into explicit relationships.

By capturing these complex relationships between entities and building context, knowledge graphs provide machines with a robust understanding of how different entities are related. Linking your entities internally and externally enriches the information available to search engines to create a holistic view of your organization.

This approach also helps prevent misrepresentation of your content and avoids machine confusion between ambiguous entities. Consider the thing, “Apple”, as an example; it could refer to the fruit or the brand. By linking your entity to the relevant external definition using the sameAs property, you offer an explicit distinction and enable search engines to align your content accurately with user queries.

Schema App Helps Define Your Entities & Develop Your Knowledge Graph

You can help search engines further understand, contextualize and distinguish the entities on your site using Schema Markup. If you are looking to leverage semantic Schema Markup to define your entities and develop a robust marketing knowledge graph for your organization, we can help.

At Schema App, we help enterprise SEO teams leverage semantic Schema Markup to define and link their entities, develop their knowledge graph, and improve search performance. Visit our website to learn more about our Schema Markup and knowledge graph solution.

Curious about how we can support your organization? Fill out this form to get started and connect with us.

The post What is an Entity in SEO? 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.

In this article, we’ll explore what Linked Data is and share examples of Linked Data projects which many in the SEO and tech community refer to as knowledge graphs.

What is Linked Data? 

Linked Data is a set of design principles for publishing machine-readable interconnected data on the web. 

The term “Linked Data” first appeared in 2006 when Tim Berners-Lee published a design note about the Semantic Web. He sought to use Linked Data as a way of representing the relationship between different things on the internet. The internet would then become a huge database of interconnected (linked) objects (data) and become the Semantic Web.

Businesses or enterprises can use Linked Data to define things and the relationships between them. For example, companies like Facebook, Twitter, and LinkedIn have undertaken Linked Data projects to represent social networks. When users perform actions like connecting to other users or liking and resharing content, these actions are reflected in a graphical representation of who they are, who they know and what they like.

As a result, these social media platforms can gain knowledge about a person and enable things like targeted advertising to users based on their relationship to other things. However, the knowledge that these social media platforms derive from their Linked Data is proprietary and not licensed for external use.

This led to a movement calling for Linked Data to be open for people to use freely for research purposes – especially from governmental organizations, and other public institutions such as museums.

In 2010, Tim Berners-Lee modified his original design note to add principles for Linked Open Data, a variation of Linked Data permitting reuse.

Linked Open Data (LOD) is Linked Data released under an open license, which allows others to freely access and reuse it.

Principles of Linked Data

When Tim Berners-Lee first published the design note about Linked Data, he defined the 4 principles of Linked Data. Based on this note, people or machines would be able to explore the web of data if it adhered to the following 4 principles:

1. Use URIs as names for things

A URI (Uniform Resource Identifier) is a string of characters that identifies a resource. It provides a consistent way to identify resources across different systems and protocols.  A resource (also known as an entity) is anything that can be identified and described, such as people, places, objects, or concepts.

2. Use HTTP [or HTTPS] URIs so that people can look up those names

An HTTP/HTTPS URI is a specific type of URI that uses the Hypertext Transfer Protocol or Protocol Secure. This means that when you access an HTTP/HTTPS URI in a web browser or through an HTTP request, you should get back information about the resource it identifies.

3. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL)

When a user or application accesses a Uniform Resource Identifier (URI), the information returned should be both meaningful and structured according to standardized semantic technologies, specifically RDF (for expressing the data) and SPARQL (for querying the data).

4. Include links to other URIs so that they can discover more things

When you create or publish data, you should include links within your data to other resources or entities (in the form of URIs). These URIs can point to related or relevant information, such as other resources, definitions, or attributes. This provides additional context about your own resources.

By following these principles, you contribute to a growing network of interlinked data across the web. This allows consumers of the data (human or machine) to gather more insights, context, and knowledge.

The benefit of using Linked Data for SEO

Linked Data is great for SEO because it can provide search engines with more contextual knowledge about your content. Search engines now look at relevancy to provide searchers with the most accurate results.

One of the most common forms of linked data on the web is Schema Markup which primarily describes webpage contents for search engines. Schema Markup uses the Schema.org vocabulary which can express RDF linked data in formats like JSON-LD.

When you use machine-readable code like Schema Markup to express the relationship between the entities on your site, it helps search engines understand and derive knowledge about your organization.

For example, if you have a page about your organization’s proprietary software application, you can tell search engines that this software application is provided by your organization by linking the URI that contains all the information about your organization to the provider property in the markup for your page.

{
  "@context": "http://schema.org/",
  "@type": "SoftwareApplication",
  "@id": "https://www.schemaapp.com/solutions/schema-app-highlighter/#SoftwareApplication",
  "name": "Schema App Highlighter",
  "description": "Use the Schema App Highlighter to customize your Schema Markup...",
  "applicationCategory": "Search Engine Optimization",
  "provider": {
    "@type": "Organization",
    "@id": "https://www.schemaapp.com/#Organization",
    "url": "https://www.schemaapp.com/",
    "name": "Schema App",
    "description": "Schema App is an end-to-end Schema Markup solution...",
    "telephone": "18554448624",
    "email": "support@schemaapp.com",
    "areaServed": "http://www.wikidata.org/entity/Q13780930",
  }
}

The URI appears in JSON-LD in the @id attribute. Your Schema Markup can be generated and authored without including identifiers (@id). Search engines like Google will still read it and make it eligible for rich results. However, by generating your Schema Markup with a URI, you can link it to other entities.

You can also link your Schema Markup to other Linked Data projects to be more explicit about the entities you are talking about on your website.

For example, if you are talking about football on a page, this can be confusing to search engines because the term football can mean different things depending on where you are in the world. You can help search engines disambiguate which football you are referring to by linking your page to the same entity described in Wikipedia, Wikidata or Google’s Knowledge Graph.

If you are talking about American football, you can use the sameAs property in your Schema Markup to link to the same entity on Wikipedia, Wikidata or Google’s Knowledge graph.

{
  "@context": "http://schema.org/",
  "@type": "BlogPosting",
  "@id": "https://www.schemaapp.com/blog/what-is-football/#BlogPosting",
  "url": "https://www.schemaapp.com/blog/what-is-football/",
  "name": "What is Football?",
  "headline": "What is Football?",
  "description": "Learn about the rules and history of Football.",
  "mentions": {
    "@type": "Thing",
    "name": "Football",
    "sameAs": "https://www.wikidata.org/wiki/Q41323",
    "sameAs": "https://en.wikipedia.org/wiki/American_football",
    "sameAs": "kg:/m/0jm_",
  }
}

However, applying Linked Data on your site can be a technically challenging task.

1. Quality – You need to keep the data up-to-date, accurate and complete.
2. Scalability – Handling this huge volume of data can be time-consuming and resource-intensive.
3. Expertise – Transforming your content into Linked Data requires knowledge of the technologies to do this work and how to apply them effectively.
4. Sustainability – You need resources to maintain the data quality.

Examples of Linked Data Projects in SEO

There are many examples of Linked Data Projects in use today. These Linked Data Projects are also often referred to as Knowledge Graphs.

Knowledge graphs are a collection of related entities expressed as RDF triples. When you use Schema Markup to express the relationship between two entities on your site, you are implementing Linked Data. When you connect the various entities on your site, you are effectively developing an internal knowledge graph about your organization. Your internal knowledge graph becomes even more robust and useful when linked to other external knowledge graphs.

Some of these external knowledge graphs are also useful for search engine optimization (SEO) purposes. SEO teams can connect their internal knowledge graphs to external knowledge graphs to tell search engines that the entity defined in this external knowledge graph is the same as the entity defined on their website.

Let’s explore some of the Linked Data Projects / External Knowledge Graphs pertaining to the SEO world.

Google’s Knowledge Graph

Google’s Knowledge Graph is a knowledge database that Google uses to provide quick answers to queries about certain topics or entities (people, places, organizations, things). This can show up in search in the form of a knowledge panel. The knowledge panel contains a snapshot of information about the topic based on Google’s understanding of the available content on the internet.

The story of Google’s Knowledge Graph starts with Freebase, a Metaweb project launched in 2007. Freebase was described as “a system for building the synapses for the global brain”. This massive knowledge base, which formally became a linked open data project in 2008, was one of the largest and most ambitious Linked Data projects of its time.

In 2010, Google acquired Freebase from Metaweb and imported Freebase’s massive knowledge base into Google’s proprietary Knowledge Graph. Soon after, Google introduced their Knowledge Graph in their famous ‘things, not strings’ article, indicating a pivot from lexical to semantic search.

The Google Knowledge Graph is a Linked Data project because it adheres to the 4 principles of Linked Data. However, the Google Knowledge Graph is NOT a linked open data project because the data is not published with an open license. That being said, it is possible to find identifiers (URIs) for entities in the Google Knowledge Graph and link them to your own knowledge graph.

How to access Google’s Knowledge Graph?

The Google Knowledge Graph has a search API that is read-only. You’ll notice the URIs in the output are structured with a “kg” namespace (which stands in for http://g.co/kg) and either /m/ or /g/ before a string of characters. These identifiers are called “mid”s, or Machine IDs, which is a legacy term from Freebase.

For example, the Freebase object for the entity Barack Obama has the mid /m/02mjmr. This same entity can be accessed in Google’s Knowledge Graph by going to https://www.google.com/search?kgmid=/m/02mjmr. The entity has the same mid in Google’s Knowledge Graph.

How is Google’s Knowledge Graph used?

Google uses its knowledge graph to improve the search experience on its search engine. When you search for something like “Berkshire Hathaway”, Google identifies the entities in your query and provides information on those entities from both its knowledge graph and other sources on the web. One of the most common sources is Wikipedia.

Wikipedia & DBpedia

Wikipedia is a free, collaborative online encyclopedia composed of more than 61 million articles. Wikipedia articles represent entities, such as people, places, events, concepts, or other things.

The URLs of Wikipedia articles also function as URIs for the entities they represent. So the URL https://en.wikipedia.org/wiki/Kathryn_Janeway is both an article that can be visited on the web, and the URI that represents the entity, Kathryn Janeway, in Wikipedia’s knowledge base.

Articles within Wikipedia contain structured elements, as well as links to other related entities. While Wikipedia on its own isn’t a traditional Linked Data project, it plays a significant role in the Linked Data ecosystem on the web, particularly with regard to DBpedia and Wikidata.

DBpedia is a linked open data project that extracts information from Wikipedia to generate RDF triples, which can be semantically queried alongside other related datasets. It pulls information from the structured elements of Wikipedia pages, such as “infobox” tables like this:

While Wikipedia might be an excellent source of summarized information for general use, the depth and breadth of information on Wikipedia means it has become an essential source of training data for many AI initiatives such as natural language processing, named entity recognition, and the development of Knowledge Graphs like Google’s Knowledge Graph.

Post from Wikipedia Engineering Manager, Joseph S.; inspired by https://xkcd.com/2347/

Wikidata

Wikidata is a collaborative Linked Open Data project that’s been operated by the Wikimedia Foundation since its inception in 2012 (source).

Despite having Wiki in its name, Wikidata is not the same as Wikipedia. Wikidata is a broader knowledge base than Wikipedia, containing data about a wider range of topics. Wikidata also allows users to create RDF Linked Data directly.

Even though Wikidata and DBpedia are both Linked Open Data projects related to Wikipedia, they have different aims and serve different functions.

DBpedia extracts information to generate Linked Data from Wikipedia’s structured sources like infoboxes. As a result, DBpedia treats the knowledge derived as facts.

Rather than extracting information from Wikipedia, Wikidata creates Linked Data for Wikipedia (source). Since Wikidata also treats statements within the Linked Data as claims rather than facts, these statements must be annotated with provenance information (i.e. who made each claim).

Instead of “mid”s (identifiers used by Freebase/Google’s Knowledge Graph), each entity in Wikidata has a “qid”.

Here’s a summary of the identifiers for each of the Linked Data projects listed above.

Google’s Knowledge Graph, Wikipedia, and Wikidata are the most common Linked Data projects utilized in SEO. When we talk about connecting your Schema Markup to external authoritative knowledge bases at Schema App, these are the external knowledge graphs we are referring to.

How to use Linked Data with Schema App

At Schema App, our semantic technologies allow SEO teams to easily generate Linked Data for their website content.

Generate URIs for your entities

When you publish your Schema Markup using the Schema App Editor or Highlighter, our tool automatically generates HTTPS URIs for the entities you define in your Schema Markup. These URIs, which appear in the @id attribute, link to the URLs of the pages where they’ve been mentioned.

For example, we publish Organization markup to our Schema App home page. The URI for our Organization entity would then be the URL of our homepage + #Organization – https://www.schemaapp.com/#Organization. If you navigate to this URI, it will take you to the page about our Organization.

Creating URIs for entities on your site allows you to easily link to those entities in your Schema Markup. For example, if your organization has published a blog post you can link your Organization URI to the publisher property in your BlogPosting Schema Markup.

{
  "@context": "http://schema.org/",
  "@type": "BlogPosting",
  "@id": "https://www.schemaapp.com/schema-markup/what-is-a-rich-result/#BlogPosting",
  "url": "https://www.schemaapp.com/schema-markup/what-is-a-rich-result/",
  "name": "What is a Rich Result?",
  "headline": "What is a Rich Result?",
  "description": "A rich result is an enhanced search result shown on search engine results page. Find out how you can achieve a rich result for your page.",
  "publisher": {
    "@type": "Organization",
    "@id": "https://www.schemaapp.com/#Organization",
    "url": "https://www.schemaapp.com/",
    "name": "Schema App",
    "description": "Schema App is an end-to-end Schema Markup solution...", 
    "telephone": "18554448624",
    "email": "support@schemaapp.com",
    "areaServed": "http://www.wikidata.org/entity/Q13780930",
  }
}

Linking to external entities

Our tools also allow SEO teams to link to external entities using a variety of methods such as:

• Manually linking to external entities using properties like sameAs and additionalType
• Automatically linking to external entities using our Omni Linked Entity Recognition feature in the Schema App Highlighter tool
• Automatically linking the categories and tags on a WordPress site to existing Wikipedia and Wikidata entities using our Advanced WordPress plugin.

You can read this article to learn more about our entity linking methods.

Overcome the challenges of implementing Linked Data

In summary, Linked Data facilitates the connection of data from different sources to provide machines with more contextual information, enabling them to infer new knowledge from existing facts.

Applying Linked Data through the Schema Markup on your site can help search engines understand the relationship between the entities on your site and disambiguate the entities mentioned in your content.

If you need help implementing Linked Data on your site, we can help. At Schema App, we provide SEO teams with the tools and expertise to implement Linked Data at scale. Get in touch with us to learn more.

The post Linked Data in SEO: What You Need To Know appeared first on Schema App Solutions.

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.

Download our guide to learn how to connect the entities on your site using Schema Markup.

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?

Get started today to learn about our solution.

The post Knowledge Graphs: The Value of Schema Markup Beyond Rich Results appeared first on Schema App Solutions.

This past August, Google first removed How-To rich results on mobile. As a result, we saw a huge decline in clicks and impressions for How-To rich results across our customers. This updated announcement will undoubtedly remove all traffic and impressions from the rich result.

Why is this happening?

Prior to this change, content publishers would add HowTo Schema Markup to pages with instructional content that defined the steps needed to successfully complete a task. If appropriate, Google would then award the page with a How-To rich result that outlined the steps in the SERP.

However, we’ve often found How-To rich results to be somewhat controversial. On one hand, rich results were supposed to increase user engagement and drive click-through rates to a site. On the other hand, How-To rich results usually provided users with the answer directly on the SERP, resulting in a lower click-through rate. As such, How-To rich results were not as widely adopted as other rich results like product, review snippets and FAQ.

That said, How-To rich results still provided users with valuable information on the SERP and could help organizations improve their customer journey. So why is Google removing this rich result from the SERP?

In their announcement, Google mentioned that this was a continued effort on their end to ‘simplify search results’. This year, Google has made some significant changes to the SERP.

• They introduced Search Generative Experience (SGE) – their generative AI-powered search interface.
• They stopped awarding video rich results to pages that did not have a video as their main content.
• They stopped awarding FAQ rich results on both desktop and mobile for most websites and will only be showing FAQ rich results for well-known, authoritative government and health websites.
• They deprecated How-To rich results entirely from the SERP.

However, this begs the question: What does Google mean by simplifying search results?

Are they trying to declutter the search engine results page? They did reduce the visibility of video and FAQ rich results in the past few months, possibly because people were abusing them. However, the SERP is still littered with advertisements, making it tougher for users to identify the most appropriate result for their query.

Or could they be simplifying search results that SGE can also provide? As seen in SEO expert, Glenn Gabe’s tweet, the content from the same How-To was shown in SGE and in the first position in the SERP as a How-To rich result.

Ah, yes. But this is about the desktop version being removed. This is how it looked. See my listing below SGE (when HowTo was still there on desktop). pic.twitter.com/S1nqtYukNa

— Glenn Gabe (@glenngabe) September 14, 2023

One of the glowing features of SGE is its ability to provide users with answers and additional relevant information that they might need. If you search up how to perform a task, SGE can provide you the steps to perform the task successfully and links to a few pages that also capture those steps.

If you search for the top Italian restaurants, SGE can provide you with a list of restaurants together with a map showing where they’re located in proximity to you, and links to aggregator sites that also have a list of top Italian restaurants in your city. These are just two of the many examples of how SGE creates helpful experiences based on the wealth of information on the web.

At its core, Google’s mission is to organize the world’s information and make it universally accessible and useful. Rich results were first introduced to provide users with more useful information in search, help them make better decisions and find answers. It was also a way for Google to incentivize website owners to add structured data to their sites to help search engines understand the content on a page.

But with SGE providing the information in a simplified way, more rich results could be rendered obsolete in the coming years. That said, this does not mean that you should abandon adding Schema Markup to your site.

What should you do next?

Schema Markup helps machines understand and contextualize the content and information on your website.

Even though you will no longer achieve a How-To rich result on your page, you should still add Schema Markup to your pages to futureproof your organization for search.

This is a paradigm shift that requires SEOs to think about the value of Schema Markup beyond rich results. 

Over the past few years, search algorithms have shifted from lexical to semantic search. Instead of ranking pages based on keyword matching, search engines are ranking pages based on the relevance of the concepts and entities in the page’s content to the searcher’s query.

And how do you identify and define the entities on your website for search engines? You can define the entities on your website using Schema Markup.

By marking up the content on your site, you are helping search engines understand the concepts and entities on your website and providing them with contextual information about these entities. In return, they can better match your page to a query and ideally improve your ranking on search in the long run.

If you are interested in learning more about entities and semantic search, you can tune in to our recent webinar with Mike King or Schema Markup expert, Dave Ojeda’s latest interview on iPullRank’s Rankable podcast.

Generative AI search engines like SGE and the new Bing still face hallucination challenges resulting in inaccurate results. At Schema App, we’ve been advising our customers to think about the semantic value of Schema Markup.

Instead of implementing Schema Markup on a handful of pages for the sole purpose of achieving a rich result, you should implement Schema Markup across your site to define the entities and concepts on your site and link them to develop your very own marketing knowledge graph.

Knowledge graphs are a structured and organized information data layer that can help search engines to improve the accuracy of their answers and provide your organization with a control point to inform generative AI on your web content. Your marketing knowledge graph can also be reused for other AI initiatives.

As the SEO industry continues to see changes from Google and on search, organizations need to prepare to stay ahead of the competition. If you are looking to learn more about semantic Schema Markup, we can help.

Contact us to see how we can help your organization build a marketing knowledge graph and future proof your organization for AI search.

If you are a Schema App customer with concerns regarding the changes in rich results, please get in touch with your Customer Success Manager to see how we can support your organization through these changes.

The post Say Goodbye to How-To Rich Results on Google appeared first on Schema App Solutions.

FAQ rich results will only be available for “well-known, authoritative government and health websites”. Other sites won’t receive FAQ rich results “regularly” (which is not the same as saying “ever”). We have yet to see what Google considers an authoritative site or health site, but this is one of the many questions we endeavour to answer in the coming weeks.

How-to rich results, on the other hand, will exclusively appear on desktop and no longer be visible to mobile users. That said, with Google’s mobile-first indexing, websites should still include HowTo markup on both their mobile and desktop site to achieve the How-to rich result on desktop.

Even though this change will not have an impact on search rankings, websites that leverage FAQ and How-to rich results will likely see a decline in traffic and impressions from these rich results on the Google Search Console performance report.

This announcement garnered strong reactions from the SEO community, as many mourned the loss of some of their best-performing (and content-heavy) rich results. In response to some of the tweets, John Mueller referenced “the tragedy of the commons”, and other SEOs remarked on the overuse of FAQs, in particular, as “spammy”.

Independent of abuse / over-use, things can change on the web / with users / with focus shifts, and it’s important to clean up from time to time. (And it’s a good reminder to read up about the tragedy of the commons :-)).

— John Mueller (official) · #MaybeABot (@JohnMu) August 8, 2023

But Mueller also recommended SEOs “really focus on structured data to make your pages eligible for a specific treatment in search. Additional structured data can be useful to understand the content better, but [he] wouldn’t assume there’s a visible effect / ranking change.”

What Schema Performance Analytics is Showing

We are monitoring the industry data from Schema Performance Analytics on a daily basis to see when the changes announced take place.

We have seen varied results after August 8th. We have seen drops in FAQ performance in some of our clients.

As of August 14, we’ve seen a decline in FAQ performance on mobile for customers in the Healthcare Industry, while FAQ performance on desktop remains stable. Stay tuned for more updates on FAQ performance.

Schema App Perspective on this Change

Here are our key takeaways from this change:

1. Focus on targeting a diverse range of rich results.
2. Continue adding connected schema markup to support machine understanding.
3. Create quality content that prioritizes a human audience.

These three recommendations are already considered best practices here at Schema App and what we already work on with our customers.

Focus on targeting a diverse range of rich results

At Schema App, we’ve seen lots of fluctuations from various rich results over time. As such, we always recommend diversifying the rich results in your portfolio.

Google’s goal is to provide searchers with the best quality results, and we’ve seen them make countless changes to the algorithm or the SERP to inch closer to that goal.

Whenever Google makes a change like this, we’ve been able to detect the drop in performance and test solutions to help our customers overcome them. For example, FAQs not being granted unless questions matched keywords exactly or videos needing to be the main content of the page to achieve a Video rich result. Our approach to this announcement is no different.

FAQ rich results have historically performed well for many of our customers. It brought more website content to the SERP and allowed the inclusion of HTML to improve the readability of the answers and embed hyperlinks. We will continue to monitor FAQ rich results to see who Google awards this rich result to and look for opportunities for our customers to qualify for it.

In the meantime, our customer success managers will work closely with our customers to identify other rich result opportunities and ways to improve their content to achieve a wider range of rich results.

Continue adding connected Schema Markup to support machine understanding

At its core, Schema Markup is a code that helps machines better understand the content on your page.

As Google and Bing accelerate their AI search capabilities, they will need to overcome the challenge of AI hallucinations to achieve a reasonable level of efficacy. By adding Schema Markup to your site, you can provide search engines with reliable, structured content that they can use to train and hone the accuracy of their Large Language Models.

Google might have eliminated the FAQ rich result but there is still an opportunity for your organization to appear in their Search Generative Experience (SGE).

If you want Google to provide an accurate answer regarding your organization in their new AI search experience, adding robust, semantic Schema Markup on your site is necessary. Doing so will allow your organization to generate a marketing knowledge graph from the content on your site. AI search engines can then use this knowledge graph to provide users with more accurate information about your organization.

Conclusion

We will be sharing more insights on the situation as Google rolls out the changes to FAQ and How-To rich results in the next week. Our team is also actively testing various alternative solutions to ensure our customers continue to see great results from implementing Schema Markup.

If you have more questions, please reach out to your assigned customer success manager or support@schemaapp.com.

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