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.

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.

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.

The post Changes to FAQ and How-to rich results on Google appeared first on Schema App Solutions.

Since our inception, we’ve been helping organizations across the globe implement connected Schema Markup on their websites to strengthen their SEO strategy.

By implementing Schema Markup, our customers can achieve rich results on search engine results pages (SERPs) and increase traffic to their sites. However, the advantages of Schema Markup extend beyond just obtaining rich results. Schema Markup is a semantic technology with numerous benefits.

In this article, we will explore what semantic technology is and how Schema App leverages it to provide meaningful context and understanding of data.

Understanding Semantic Technology

Semantic technology is a set of technologies, methodologies and standards that provide meaning (semantics) to data. It achieves this by representing relationships between different categories of entities, transforming raw data into knowledge.

By providing context, semantic technology enables machines to better comprehend and interpret data, leading to more intelligent decision-making processes.

Background and Terminology

Before jumping into how Schema App is semantic, it is essential to familiarize ourselves with key terms in the field of semantic technology.

Here is a glossary of terms that will be referenced throughout this article.

RDF (Resource Description Framework)

A framework used to express data as a directed graph using subject-predicate-object statements known as triples.

By combining these triples, vast interconnected graphs of resources can be created. This is done using Uniform Resource Identifiers (URIs).

Uniform Resource Identifier (URI)

A URI is a string of characters that identifies a resource. It provides a consistent way to identify resources across different systems and protocols.

A URL is a string of characters that both identifies a resource and where it’s located on the web. Therefore, a URL is a type of URI.

JavaScript Object Notation for Linked Data (JSON-LD)

JSON-LD is a serialization format for expressing RDF data. In simple terms, it’s one way to describe the subject-predicate-object statements. It also happens to be Google’s preferred format for consuming Schema Markup/structured data.

Ontology

An ontology defines the types of entities that can exist within a dataset, and the properties that describe and connect these entities. Schema.org is an example of a loose ontology, serving as a vocabulary rather than imposing strict logic constraints like other formal ontologies.

Knowledge Graph

A knowledge graph is a structured representation of information that captures the context and connections between entities, their attributes, and the relationships between them.

Schema App uses JSON-LD to express how the Schema.org “ontology” defines the connections in your data (in our case, your content).

As a result, content on your page that states things like “this DeWalt Handsaw is from the brand DeWalt” and you can express that in JSON-LD to help search engines understand that statement.

When you connect multiple entities using these technologies, you are constructing a knowledge graph.

Search engines can then interpret the relationships between entities through these knowledge graph connections, enhancing their understanding of the content on your site. More recently, knowledge graphs are also being explored as a means of grounding LLMs to prevent hallucinations in Generative AI.

So as you can see, these technologies are powerful sources of meaning (semantics) for machines like search engines.

The most foundational resource that both uses and is, in itself, a semantic technology, is Schema.org. Schema App utilizes the Schema.org vocabulary to help our customers translate their content to a language understood by search engines.

What Makes Schema.org a Semantic Technology?

Schema.org was founded in 2011 by Google, Bing, Yahoo and Yandex as a way to translate messy human language into structured, machine-readable language. This language is now supported by all major search engines, improving their ability to match search queries with relevant results.

Search engines have shifted to using semantic SEO to provide more accurate and relevant results to users. Instead of matching keywords in an article to search queries, search engines now understand the meaning (semantics) of the content on a page and identify if the content matches the searcher’s intent and query.

In light of this, Schema.org was developed as a vocabulary of types and properties to clearly describe things on a site and provide context on how these things are connected to each other.

Types

The Schema.org types are organized into a hierarchy, starting with Thing and then providing more specific subtypes from there. Example: Thing, which has the subtype Person, which has the subtype Patient.

Properties

Each type has a list of available properties to further describe it. In the image below, we can see that the Person type can be further described with properties like address, alumniOf, and birthDate.

Expected Types for Properties

Most properties also have specific types they can connect to. For example, a Person can have an address property which states the physical address of where the person is located. This information can either be added as plain text, or it can be used to link to the PostalAddress type which has its own page in Schema.org.

By connecting different Schema.org types on your site through the properties, you are defining the relationships between entities described in the content on your site and helping machines understand it.

At Schema App, we apply the Schema.org vocabulary (a loose ontology) to customer content, expressed in JSON-LD (a semantic technology) so that search engines can explicitly understand connections between things (semantics!).

Machine-Readable Representations of Schema.org 

Under the hood, the individual terms on the Schema.org website also have ”machine-readable definitions…available as JSON-LD, embedded into the term page[‘s] HTML”.

If developers are interested in implementing the vocabulary for their own purposes, Schema.org provides downloadable “Vocabulary Definition Files” available in “common RDF formats” like JSON-LD, Turtle, Triple, or RDF/XML. Here’s a link to what the Schema.org vocabulary looks like as a JSON-LD file.

Schema.org has two interfaces – one for humans to navigate and understand, and another for machines to understand the content within their database. This is a great illustration of how semantic technology works to bridge the gap between human language and machine learning.

By understanding semantic technologies, companies like Schema App are better able to create applications and systems that leverage the best aspects of these technologies. At Schema App, we use these files in the construction of our authoring tools and implementation of Schema Markup. This allows us to organize customer information in a meaningful way to machines.

Schema App’s expertise in semantic technologies is evident in our numerous tools and features.

Schema App Tools and Features That Make Us Semantic

Here are some of our tools and features that make us semantic.

Schema App Editor & Highlighter

The Schema App Editor and Highlighter are two Schema Markup authoring tools created by our team. The Schema App Editor allows SEO teams to generate Schema Markup in JSON-LD and automatically deploy it to an individual web page without writing a single line of code.

All the pages optimized with Schema Markup can be updated and live on the site in minutes with this tool, making it easy to manage. The Editor contains the entire Schema.org vocabulary and creates connected Schema Markup via embedded data items to allow our customers to build out their knowledge graphs.

The Schema App Highlighter is also a no-code Schema Markup authoring tool, but it is for templated pages and dynamic content rather than individual URLs. With this tool, you can automatically apply descriptive Schema Markup at scale to thousands of pages and dynamically update your Schema Markup based on the content on your page.

What makes our authoring tools semantic?

Both the Editor and the Highlighter have the same semantic features, just applied in a slightly different way.

Using the Schema.org vocabulary

The primary feature that makes our authoring tools semantic is how they author markup using the Schema.org vocabulary to express RDF triples (subject-predicate-object statements) in JSON-LD.

These statements (aka semantic triples) can be combined to create huge graphs of interconnected resources using URIs (Uniform Resource Identifiers). Earlier in the article, we saw a simplified version of the triples. The image below is a more accurate representation of the triples, where the URIs are the entities being described in the graph.

By doing so, they leverage Schema.orgs’ means of translating human-readable content to machine-readable content, supporting the extraction of meaning (semantics) from web content.

All of Schema App’s authoring tools are Ontology-driven applications. Therefore, any updates or modifications to the Schema.org vocabularies will be reflected in the tools. For example, if Schema.org introduces a new property for a specific Type, the new property will be available in Schema App’s authoring tools.

Entity Linking Features

The Highlighter utilizes an automated Linked Entity Recognition feature to identify entities on the page and link them to Google’s knowledge graph and Wikidata definitions, while the Editor employs a manually applied Entity Linking Method. Both tools then nest the entities within the Schema Markup.

By using Entity Linking Methods like Linked Entity Recognition, our authoring tools can help search engines better contextualize the topics on your site and align them with the searcher’s query.

Linked Entity Recognition

As previously stated, Linked Entity Recognition (LER) is a powerful feature that can be applied to a Highlighter template to enhance content analysis.

Once applied, this automated process identifies named entities (such as people, places, things, and concepts) in content. It then links them to external identifiers from authoritative knowledge bases (like Wikipedia and the Google Knowledge Graph). These identifiers are automatically embedded within your Schema Markup.

Through the automatic embedding of these identifiers into the Schema Markup, the entities contribute valuable semantic information to the metadata. Consequently, Google and other web crawlers gain a deeper understanding of the content, thanks to the inclusion of well-defined, linked entities. This reduces ambiguity in the interpretation of content and supports more accurate matching to user queries.

For instance, we can say the DeWalt Handsaw is from the brand DeWalt, which is the same as the DeWalt described in this Wikipedia entity.

By linking the DeWalt Handsaw to the corresponding DeWalt entity on Wikipedia, search engines can clearly understand which DeWalt you are referring to.

Advanced WordPress Plugin

Like our other authoring tools, our Advanced WordPress plugin provides markup using the Schema.org vocabulary. The plugin can automatically generate Schema Markup for pages and posts. It also provides users access to our Schema App Editor for further Schema Markup customizations.

The Advanced WordPress plugin also has the ability to map WordPress tags and categories to Wikipedia and Wikidata entities to help search engines better match content with relevant search queries on those topics.

Schema Paths Tool

The Schema Paths Tool is a free tool created by the Schema App team to help users identify the best way to connect and organize different Schema types together within their Schema Markup. This is especially useful when you’re unsure which properties are available to connect two different Schema.org Types.

The Schema App team identified a need for this within their suite of tools because Schema Markup is most beneficial when it’s highly descriptive. One of the best ways to do this is by connecting your Types with the most descriptive property. The Schema Paths Tool helps you narrow down what properties each Type has that allow them to connect to one another (as an “Expected Type”).

For example, the Schema.org Organization type has more than 50 unique properties. If you want to connect an Organization to a Service it provides, you can enter both Types into the Schema Paths tool, and then receive a list of properties that can be used to connect these Types.

The Semantic Nature of Schema App

By embracing semantic technologies, Schema App helps you develop a reusable knowledge graph which also enables machines to better comprehend and interpret website content. When search engines have a clear understanding of what your page is about, it can provide searchers with more accurate and relevant results.

Our passion for semantic technologies doesn’t end with the tools and features currently available to our users. We pride ourselves on working towards a data-centric architecture for our internal data as well (see Semantic Arts Data-centric architecture manifesto) and invest time considering the possibilities of semantic technology and how we can support them.

Interested in learning more about how our tools can support your semantic SEO initiatives? Get started here.

The post What Makes Schema App Semantic? appeared first on Schema App Solutions.

Fast forward to today, search engines now prioritize positive user experience and ‘people-first’ content. Search engines consider content depth, meaning (aka semantics), and how it answers user questions by providing the desired information.

Businesses must adapt to this evolution of search. As search engines become more sophisticated, incorporating semantic understanding into your search engine optimization (SEO) strategy is crucial to keep up with the changing landscape. This will help ensure your content remains relevant and visible to your target audience.

Understanding Semantic SEO

The word ‘semantic’ is all about understanding the meaning of language.

When people use the term ‘arguing about semantics’, they’re usually debating the interpretation (or misunderstanding) of words or phrases. Semantics is a field that examines how language conveys meaning and follows certain rules for effective communication.

Semantic SEO is the process of giving more meaning and context to your web content to help search engines gain a better understanding of your content.

Why is Semantic SEO important?

The way that search engines understand your content has changed

Historically, Google solely used keywords to evaluate a web page’s topic and relevance to a search query. As of Google’s algorithm changes made in 2013, however, instead of only looking at keywords to understand what the page is about, search engines now read and understand a page’s overall topic.

This change allowed search engines to provide users with a better search experience and ensure that the results presented are providing users with the answers they are looking for.

To improve your ranking and web traffic

By utilizing semantic SEO, search engines can better understand your content and more accurately relate it to search queries. In return, your pages can rank higher on relevant searches, leading to more impressions and, ideally, more clicks. 

Because it presents users with the most relevant information based on their queries, those who do visit your pages are more easily converted into customers. This is because it’s more likely to be exactly the information/product/service they were seeking. 

To keep up with generative AI search

Semantic SEO is the future of search, and that future has already begun. The emergence of powerful generative AI search engines like Google’s Search Generative Experience, has propelled semantic technology to unprecedented heights.

In this transformative era with the AI revolution and search generative experience, search engines are gaining an unprecedented ability to interpret the nuances and meaning of human language. As a result, search queries are now returning dynamic and tailored results with the potential for conversational follow-up answers.

While traditional SEO practices, including keyword research, remain valuable in digital marketing, integrating semantic technologies like Schema Markup into your strategy can provide a competitive advantage.

By doing so, your pages become more visible and comprehensible to the intelligent systems that bridge the gap between your content and human users.

How is Semantic SEO Different From Traditional SEO?

Where traditional SEO prioritizes content that is keyword-based, semantic SEO is a topic-based approach that increases the likelihood of connecting users to information that is most relevant to their search query. 

It accomplishes this by focusing on both the meaning behind queries and the contextual information and relationships in the content being retrieved. This results in a better user experience which can lead to a lower bounce rate, as those who end up on your page from search have a higher intent to consume the information presented.

Semantic SEO is the bridge between your content and users’ intent. This is the biggest difference between Traditional SEO and Semantic SEO. – WeDevs

Moving from a keyword-based to a topic-based approach with your content can seem a bit abstract at first. After all, it’s simple enough to do some keyword research, find a list of terms, and then write content to string the terms together.

These same skills are still essential when it comes to semantic SEO, with one key difference: entities.

What are Entities?

To put it plainly: entities are things, and things have dimensions! 

They take up space (be it physical, digital, or conceptual). They also have attributes (like colour, size, duration) and, most importantly, they are understood in relation to other things.

Take, for example, “bestgihrtie”. This is a string of characters and it means nothing to a human brain, so it won’t mean anything to a search engine either. But if I decide it’s the name of my new album, snackfood, or generative AI tool, this jumble of letters now becomes an identifiable entity. In other words, the string becomes a thing.

However, that entity needs to be described for it to have any meaning. “ChatGPT” didn’t mean anything until we started hearing about it in relation to generative AI, chatbots, and productivity. 

This same entity took on a different meaning when we heard about it in relation to hallucinations, misinformation, algorithmic bias, and plagiarism. The word “relation” is doing the heavy lifting in this example since what it’s providing is context. 

We as humans use context clues to make sense of new things and search engines are doing the same thing.

That being said, machines, including search engines, aren’t good at understanding in the same way that human brains can. Search engines use natural language processing (NLP) to analyze the proximity and frequency of certain terms, phrases and entities.

There are ways, however, to make statements about entities more explicit for search engines.

Elevating Search with Entities

As previously stated, semantic search goes beyond traditional keyword matches and focuses on delivering topically relevant search results.

Instead of simply providing “plain blue links” to web pages, it can present information in various formats, such as Knowledge Panels, Featured Snippets, and Rich Results, all centered around the primary entity being searched.

This approach aims to provide users with more comprehensive and contextually relevant information related to their search query. Let’s look at an example of how a search for “Gibson Les Paul” yields results about this particular entity.

Under the “People also ask” section, we can see queries that don’t blatantly name the type of guitar, like: “How much did Kirk Hammet pay for Greeny?”. 

Greeny is a 1959 Gibson Les Paul Standard, named after its original owner, Peter Green. It happened to be purchased by Kirk Hammet, the guitarist of Metallica, which also explains the inclusion of the question “Who is the richest member of Metallica?”, which has nothing to do with guitars at all.

But if we think about this information as being derived from entities that are related to one another, the inclusion of these “People also ask” queries make sense.

And if we search for “Greeny guitar”, we’ll get a Knowledge Panel conveying some of the attributes of this particular guitar, including the fact that its manufacturer is “Gibson”.

Leverage Schema Markup to Improve Your Semantic SEO

There are many things you can do to implement semantic SEO. A lot of it involves creating clusters of content surrounding the topic that you want to be known for.

However, in addition to this, you need to ensure search engines understand what your content is about and how the entities in your content are connected. Implementing Schema Markup allows you to categorize entities and explicitly relate them to each other, providing search engines with helpful contextual information about your content.

Schema Markup, also known as structured data, is a standardized vocabulary that search engines analyze to understand the content on your web pages. By implementing Schema Markup through code, such as JSON-LD, search engines can contextualize your content and present it to users searching for relevant and related topics.

While machines don’t interpret information like humans do, Schema Markup helps bridge the gap. It does this by providing explicit details about the content on your pages, ensuring search engines accurately comprehend the topics of information your website offers.

One of the most common uses of structured data is the application of the Schema.org vocabulary expressed in JSON-LD. It’s usually found under the “technical SEO” umbrella, and most would know it as the “Thing” responsible for rich results.

Rich results can drive higher click-through rates with their engaging visuals, but if that’s the extent of your Schema Markup application, your semantic SEO strategy is missing out!

So how can you leverage Schema Markup to improve your semantic SEO?

1. Implement more specific Schema Markup to clearly explain what your page is about

To be semantic, search engines need to clearly understand your content.

Content publishers often use generic Schema Markup plugins to add default Schema Markup on certain pages like blog articles, product pages, home page, etc. However, the downside of doing this is the lack of control over your Schema Markup.

Generic Article markup autogenerated by plugins won’t give your content the richly descriptive Schema Markup that best supports the search engines.

Plugins are usually CMS-specific and tend to map more general properties to available metadata (like author, or datePublished). While these properties are still helpful, they don’t describe the content with as much depth as more specific properties like about or mentions, which can be used to call out topics and entities in an Article.

Your markup will also often be disconnected. Each page may have Schema Markup describing the content, but not necessarily how that content relates to other pages across your website.

2. Add @ids in your Schema Markup

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.

In the JSON-LD syntax, @id is used to provide URIs (uniform resource identifiers) to entities in your Schema Markup. These identifiers allow you to refer back to entities as you build your knowledge graph.

In the example below, the Organization entity created for Schema App’s homepage has the @id “https://www.schemaapp.com/#Organization”. If a blog post on another page wants to say that it was published by the Organization Schema App, the Schema Markup for that page would say the publisher is “https://www.schemaapp.com/#Organization”.

@ids give the entities in your markup unique identifiers.

Think of it like your social insurance number! There may be 10 different people named “Jane Doe” in your organization, but each of them will have a unique ID that differentiates them. Schema App auto-generates @ids for every entity, so you can link the unique entities across your website.

Therefore, if you want to improve your semantic SEO, you should add @ids to your JSON-LD Schema Markup.

3. Connect your Schema Markup to develop your knowledge graph

Establishing a connection between your Schema Markup elements is crucial for developing a comprehensive knowledge graph. Knowledge graphs are necessary for describing how things on your site are related to each other, as well as other things on the internet.

It makes your content more semantic and provides search engines with contextual knowledge about your content.

Connect Your Entities On Your Website

On your website, you can connect different entities to one another. For instance, if you have a law firm with multiple service pages, it’s important to connect those service pages to your organization. This indicates that your organization provides all of those services despite them being on separate pages.

To ensure accurate representation, it’s vital to describe the relationships between marked-up entities in detail. For example, you need to clarify if an article is about a specific topic or if it simply mentions it.

Schema App offers a free Schema Path tool that helps identify available properties to connect your entities effectively.

Connect Entities to External Authoritative Knowledge Bases

You can also connect entities on your site to external authoritative knowledge bases such as Wikidata or Wikipedia. By doing so, you are clearly explaining what your entity is about.

For example, let’s say your page talks about football. Football can mean two different sports to different readers. In America, football is American football while in Europe, football is soccer.

So if your page is about American football, you can link it to the Wikidata entity (https://www.wikidata.org/wiki/Q41323) for American football in your Schema Markup using the sameAs property. This will help search engines understand that your page is referring to American football and reduces the risk of misinterpretation.

By connecting entities on your site to other entities and external knowledge bases, you are forming your own knowledge graph. The @ids that we mentioned earlier clearly identify the entities in your content, allowing you to connect them and build context.

With Schema App, you have the flexibility to add these entities either manually through our Editor or automatically through the Highlighter, utilizing the Linked Entity Recognition feature. For WordPress users, our WordPress plugin can automatically identify and link entities that you have included in your tags and categories.

Download our Guide to Connected Schema Markup to learn how to connect the entities on your site and build your knowledge graph. 

The Future is Semantic

When creating website content for SEO, it’s important to prioritize semantic SEO that focuses on topics rather than just keywords. Search engines now understand context, relationships, and user intent better than ever before.

To stay competitive on SERPs, you need to create relevant, high-quality content that targets specific topics and use connected Schema Markup to help search engines understand how your content relates to user intent, search queries, and other information on the internet.

By embracing semantic SEO, you align your strategy with search engines’ evolving understanding. This leads to better visibility on the SERP and the delivery of highly-tailored content to your target audience.

If you’re looking to implement connected Schema Markup at scale for your site, get in touch with our team to learn about our solution.

The post Semantic SEO: What You Need to Know appeared first on Schema App Solutions.

1. Lean into Semantic Search

Back in 2008, SEOs were more focused on keyword stuffing and adding as many backlinks as possible to improve search rankings. The goal was to appear for as many related terms in the highest position possible on SERPs (search engine results pages). Over ten years later, the shift towards semantic search and a more entity-based approach has become a best practice for advanced SEO.

In September 2013, Google announced the algorithm update Hummingbird, designed to handle more complex queries. This was the first time the search algorithm was rewritten in about 12 years. While Hummingbird did not seem to have a negative effect on website performance, SEOs started noticing that the algorithm update was having a positive effect on the accuracy of Google’s Knowledge Graph, which is a digital database of information from sources Google trusts—launched just one year before the Hummingbird announcement. Google was placing more importance on search intent when supplying information in the SERPs. For Google to produce results that will satisfy a user’s search intent is where semantic search comes in.

What is Semantic Search?

Semantics is the philosophical study of meaning. Semantic search is the process of search engines generating the most accurate SERPs possible by looking at the intent and context of a user’s search query. It’s artificial intelligence attempting to understand natural language the way a human would. Each search query is driven by something, and it’s our job as SEOs to understand that motivation. For example, if someone uses the phrase “how to” in their search query, that user is looking for a list of steps and will focus on the most relevant, helpful content in the SERPs.

PRO TIP

Add HowTo structured data to your how-to content to stand out from the competition with enhanced search results.

Tip #1

Stop keyword stuffing and start evolving your content to meet the “something” people are looking for.

2. Consider User Intent in your Content Strategy

You want your content type to match the query. For a “how to” query, you need how-to content on your site; for a “frequently asked question”, you need FAQ content. Not only will this satisfy search intent, but it will also help search engines understand the context and relevance of your content, so that Google can better match search queries to information on your site.

You can help Google contextualize your website content with a technical SEO practice called structured data, also known as schema markup. Schema markup is a form of microdata that provides additional information to search engines when added to your website. When all required and recommended properties are added, your content will be eligible for enhanced search experiences called rich results, making your content appear more prominently in the SERPs.

Schema markup is especially important during the age of Hummingbird—when how Google interprets the context of a query will determine the quality of a search result.

We like to tell our customers that structured data should be a part of their content strategy. As you consider the motivations and pain points of your customers and develop content to satisfy their search intent, build the structured data in tandem for a more effective process. That way, your new content has more potential to match the relevant search query AND to engage users with enhanced search results.

Tip #2

Consider the motivations of your customers; then, satisfy their pain points with content and use schema markup to make that content stand out in search.

3. Build Connections with Google’s Knowledge Graph

Hummingbird uses natural language processing to ensure that entire pages of content match search intent, not just a few words on the page. For this reason, creating quality content that is helpful and informative for your customers is more effective than just including popular keyphrases on your web pages to rank higher in SERPs. By creating connections with structured data between your content and entities defined in Google’s Knowledge Graph, you can help search engines better relate your information to what a user is searching for.

What is a Knowledge Graph?

A search engine’s knowledge graph is a database of facts about people, places and things from trusted sources like Wikipedia. Google’s Knowledge Graph allows it to easily answer factual questions with publicly available information, like the height of the Eiffel Tower, for example.

How does Google’s Knowledge Graph Work?

The Knowledge Graph Search API lets you find entities—people, places and things—defined in Google’s Knowledge Graph. You can define objects on your web pages as distinct entities with their own properties and relationships to other entities through structured data like schema markup. Once defined, link your entities to Google’s Knowledge Graph with structured data so that search engines can easily relate your information to a user’s intent.

What is the difference between a Knowledge Graph and a Knowledge Panel?

It’s easy to get confused between the Google Knowledge Graph and Google Knowledge Panels. Sometimes, Google search will show special boxes with information about people, places or things. These are Google Knowledge Panels:

These information boxes appear in search results when Google recognizes an entity in a user’s search query. The information in Knowledge Panels comes from Google’s Knowledge Graph.

PRO TIP

Did you know that schema markup can enhance your Google Knowledge Panel? Learn how in our blog post here.

Hummingbird is arguably the beginning of the semantic search era as we know it today. Semantic search allows Google to distinguish between different entities, which is why adding structured data to your website can help streamline this process. The more context you can offer search engines, the better.  This way, Google can better match search intent with information on your website.

Tip #3

Connect well-defined entities on your website with Google’s Knowledge Graph to create context and relevance for your brand.

4. Start Building your E-E-A-T

The content on your website should showcase your experience, expertise, authoritativeness, and trustworthiness—your E-E-A-T—in a particular field. This content should support both your SEO and overall business goals.

What is E-E-A-T?

Google first introduced the concept of E-A-T in its 2014 edition of Search Quality Guidelines. In December 2022, a new iteration of E-E-A-T was introduced, where an extra ‘E’ was added to emphasize the importance of demonstrating ‘experience’ in website content. 

The ultimate goal of a search engine is to return accurate, truthful, and useful information to satisfy search intent, and E-E-A-T fundamentally supports that. It’s an important factor that Google uses to evaluate the quality of a web page. By increasing your E-E-A-T on and off your site, you have the opportunity to improve your Google search rankings.

Tip #4

Create quality, connected content on your website that supports your experience, expertise, authoritativeness and trustworthiness in a particular field. Learn more about how to create connectedness with structured data from Schema App CEO Martha van Berkel:

Expertise

Your brand’s expertise is largely evaluated at the content level. Your website content should be created by recognized subject matter experts, including your brand founder, content writers, and contributing experts in your field.

PRO TIP

Mark up the author’s byline with the Author schema property to streamline Google’s evaluation of the E-E-A-T for that web page. Learn how here.

Experience

Similar to expertise, experience demonstrates the firsthand knowledge that your content creator has regarding the topic on the page. By highlighting the firsthand experience with the topic, you can reinforce the trustworthiness and relevance of your content for search engines.

Authoritativeness

Create connections with other experts in your field. With shared content opportunities—like guest blog posts, webinars, podcasts, etc—you have a new sphere of influence. Invite your collaborators to add backlinks for your brand on their website—and do the same for them!

Trustworthiness

Readers are looking for transparent, legitimate information to satisfy their search intent. One easy tactic is to ensure that the authors for any articles or news posts are explicitly mentioned.

To learn more about how to increase your E-E-A-T with schema markup, check out our blog post here.

Revolutionize your Content Strategy for Healthier SEO 

Instead of focusing on creating content around keywords, start thinking about broad topics within your niche that you can cover in depth. What are the pain points you solve, and how can you develop content to communicate these solutions? While it can be a challenging shift in mindset for your content strategy, switching from keywords to topics will help you create more quality, evergreen content for your website.

We challenge you to dig even deeper into your SEO journey. Google recommends adding structured data to your web pages so that it can better understand the intent of your content. While your content strategy is fundamental for effective search engine optimization, remember that you are writing content for humans AND for search engines to understand.

The post 4 Actionable Structured Data Strategies for Busy Digital Marketers appeared first on Schema App Solutions.

Search engine optimization is a long-term investment, but during periods of change in user behavior SEO can also be your greatest short-term ally. Remember that SEO doesn’t just increase traffic to your website; it also drives higher-quality traffic. Users finding their way to your site are interested in what you have to offer because of how you’ve communicated your business in search, underlining how SEO helps you identify more qualified leads for your business. 

For example, many patients begin their healthcare journey in Google search. When choosing a provider, they want to know things like the area served, the contact hours, how to book an appointment, and any medical specialties.

With structured data markup, you have more control over what information appears in search engine results pages, and how it appears. This additional information is displayed through enhanced Google search features like rich results, that allow you to stand out from your competition.

What is Structured Data Markup?

Structured data markup is metadata that can be added to the backend of your website. In SEO, this usually refers to Schema.org, a specific structured data vocabulary created by Google, Microsoft, Yandex and Yahoo! back in 2014. This standardized vocabulary is always being updated with new types and properties to categorize and connect data.

The fact that Google continues to invest in schema markup demonstrates that it’s an investment worth making for your website. It essentially provides an enhanced description of your content that allows search engines to relay additional information in search through engaging rich results and other Google features.  The more that Google understands, the better it can match your content to a user’s search query.

When getting started, it helps to think of structured data as a way of defining entities across your web pages, and connecting them in a graph. Your homepage will be your primary entity, or entity home, so it’s best to add markup to that page first. Typically, you’ll want to mark up your homepage as either an Organization, LocalBusiness, or a more specific subtype. Our Ultimate How-to Guide for Local Business Schema Markup can help you establish which type to use, and which properties are most useful for describing your homepage. 

One of the benefits of LocalBusiness schema is that Google uses the information in your markup to enhance your local Google Knowledge Panel, which is the information box that appears on the right side of desktop search results, and at the top and throughout the search engine results page (SERP) for mobile devices.

Schema Markup Enhances your Google Knowledge Panel

If you’re a small or medium-sized business offering local or essential services, keeping your Google Business Profile (GBP) up-to-date is so important during times of change. Marking up your website with LocalBusiness structured data can also enhance your Google Knowledge Panel. Most consumers don’t become customers immediately, which is why increasing your findability and viewability in search engine page results will help you nurture that customer journey from start to finish. 

It’s important to note that there are many subclasses of LocalBusiness schema, including AnimalShelter, ChildCare, Dentist, ShoppingCenter, and more! See the full list in our guide. Try to be as specific as possible when defining your type of  business, but if there isn’t an existing type at schema.org LocalBusiness will do just fine.

Demonstrate your credibility by letting customers leave reviews, and then mark up that content with Review structured data while following Google’s structured data guidelines. Here are some important technical guidelines for Review schema markup to keep in mind, pulled from Google’s documentation:

• Mark up an aggregate evaluation of an item with AggregateRating schema
• Refer to a specific product or service by nesting the review in the markup of another schema.org type, like Book or Recipe, or by using a schema.org type as the value for itemReviewed
• Your reviews and ratings should be immediately available to users from the marked up page
• Reviews should be about a specific item, not a category or list of items
• Don’t aggregate reviews from other websites
• For a LocalBusiness or Organization, your markup is not eligible for star review features if the entity being reviewed, aka your local business or organization, controls those reviews. In other words, don’t review yourself and mark those reviews up with structured data! 
• Your ratings should be directly sourced from users
• Don’t use human editors to create, curate, or compile ratings or reviews for a local business

Learn more about creating review schema markup in our guide.

You’re building trust and familiarity with your brand, both with your customers and with search engines. Publishing quality content engages and converts more quality leads, and the more engaging touchpoints you have, the more likely quality prospects will get in touch with you.

Diversify Your Rich Results Portfolio

In the height of COVID and vaccinations, people need reliable and up-to-date information. Google introduced the SpecialAnnouncement schema.org type, the markup of which was used for urgent announcements published by locally-oriented organizations such as schools, pharmacies, healthcare providers, community groups, police, and local governments. Learn more about implementing SpecialAnnouncement structured data in our Guide to COVID-19 Structured Data.

On May 20th, 2021, we suddenly saw this type of rich result drop off. That is why we recommend diversifying the rich results that your content is eligible for. We expect medical organizations to create new content that is in line with any changes in the healthcare industry, but making sure that rich result eligibility is part of your content strategy will set you up for success in terms of your search engine optimization performance. 

Here is a list of the rich results available through Google’s Search Gallery:

To learn more about each rich result opportunity, click on the links in our list above or explore the Search Gallery here.

It is even possible to achieve more rich result opportunities through multi-type entities. A multi-type entity (MTE) is one entity that is defined using multiple schema.org types (though usually not more than two). Creating a multi-type data item allows you to utilize all the properties available to both types. You may want to create a multi-type entity for your business if, for example, you’re using the Physician Local Business type and want to add alumni information that’s only available to the Person type. To resolve this, your local business would be typed as both Physician and Person.

Diversification of rich results prepares your SEO team to switch focus from one search enhancement to another whenever there are updates to Google’s structured data documentation, demonstrating the agility a robust structured data strategy provides.

Google Continues to Invest in Schema Markup

As we mentioned above, Google continues to demonstrate their investment in schema markup by regularly updating their structured data documentation, and introducing new schema.org properties.

We always get excited about new schema markup properties, because that means more opportunities for our customers to maximize their results from structured data! Your schema markup should stay updated and in line with the visible content on your website, so as not to violate any of Google’s structured data guidelines.

There have already been two hearty schema.org releases this year, but in 2020 we saw eleven releases, so we are expecting many more to come before the end of 2021. Some of our favorites from this most recent release include a number of terms proposed by the Bioschemas project, additions around ecommerce returns and job postings, and the addition of startOffset to the SeekToAction schema.org type for Videos. Read more about this latest release in our news post.

Keeping an eye on new schema.org releases and updates to Google’s structured data documentation will help you diversify your rich result portfolio, as you never know when the next opportunity for your content to stand out in search will come along. Having the foundation in place across your site gives you a head start. Sign up for the Schema App Newsletter to receive regular updates to the schema markup vocabulary and Google’s structured data guidelines.

Example of Healthcare Client being Agile with Structured Data

Here’s an example. One of our healthcare clients is currently restructuring their service lines and simultaneously using structured data to inform their content strategy by targeting different audiences in different geographic locations. This is one way structured data can make your SEO team more agile. 

Through structured data, they’re implementing a number of pending schema.org types and properties that were introduced to support medical content. This follows our best practice of diversifying your rich result portfolio by remaining informed of any updates to the schema.org vocabulary.

SEO is especially important for healthcare organizations, as their success relies on how easily users can find them online. In August of 2018, Google rolled out a core update named the “Medic” update, which disproportionately affected sites in the health and wellness vertical as part of a large-scale impact across all verticals. Structured data has become even more strategic for healthcare organizations as they try to regain any SEO ground lost from said update.

How the Schema App Highlighter Increases Your SEO Agility

At Schema App, one of our key values is agility. Technology is advancing quickly, but the ability to be agile means we are prepared for anything at any time. That’s why we created the Schema App Highlighter, a world-class, scalable structured data authoring and management platform. 

One of the best parts about the Highlighter is that you don’t need to write a line of code! The tool does it for you at scale and with accuracy. Because the Highlighter is dynamic, any changes made to your content will be reflected in your structured data automatically, and any changes to Google’s Documentation will be reflected in our tools. If you make any changes to your website structure, the Highlighter can accommodate this with ease. You’ll also be working with a schema markup expert from our Customer Success team, who is always up to date with Google’s changes to structured data so you don’t have to be.

We have seen significant increases in how our physicians are being found. Physician bio clips increased 90% from 150,000 clicks to about 285,000 clicks and we saw a 38% increase in the click-through rate of the search results as well”

— Brandi West, Executive Director, Digital Brand & Content Strategy I Digital Marketing, AdventHealth

How does your website content currently look in search results? How do you want it to look? Structured data markup gives you more control over what content appears in search, and how it appears. A diverse portfolio of rich results will empower your SEO team to be more agile, letting them focus on strategy and when/where to add more structured data markup to your website instead of chasing after trending keyphrases for short-term boosts of traffic.

We’ve helped customers across multiple industries diversify their digital strategies with a robust schema markup portfolio, but especially in healthcare where the rich result opportunities are vast, and building trust with patients is important. If you need help getting started with your structured data markup or want to analyze the rich result opportunities on your website, set up a call with our technical experts today!

The post How to be Agile with Structured Data in a Changing SEO World appeared first on Schema App Solutions.