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

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

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

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

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

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

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

Understanding Content Knowledge Graphs

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

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

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

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

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

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

Content Knowledge Graph Use Cases

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

Improve Content Inventory Organization

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

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

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

Content Coverage and Gap Identification

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

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

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

Use Case 1: Aligning Content With Business Goals

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

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

• HowTo
• BreadcrumbList
• FAQPage
• VideoObject

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

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

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

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

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

Use Case 2: Assessing and Revising Content Coverage

Consider another example from one of our healthcare customers:

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

Armed with this information, the customer was able to:

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

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

Disambiguating Entities to Ensure Brand Name Consistency

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

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

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

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

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

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

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

Schema App Helps Develop Your Content Knowledge Graph

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Achieve rich results and stand out in search

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

Building Your Content Knowledge Graph

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

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

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

Step 1: Knowledge Creation

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

Have high-quality content on your website

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

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

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

Marking up your content using the Schema.org vocabulary

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

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

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

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

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

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

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

Step 2: Knowledge Hosting

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

Collecting the Schema Markup

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

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

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

But where does this storage occur?

Storing Data

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

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

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

Retrieving Data

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

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

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

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

Step 3: Knowledge Curation

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

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

• Accessible
• Correct
• Complete

Let’s break those down further.

Accessible

The data in your knowledge graph needs to be available.

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

Correct

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

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

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

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

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

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

Complete

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

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

You can apply entity linking:

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

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

Step 4: Knowledge Deployment

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

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

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

Enhancing User Experience

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

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

Content Optimization

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

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

AI and Machine Learning Applications

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

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

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

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

Developing a Content Knowledge Graph for Your Organization

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

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

Get in touch with our team to learn more.

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

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.

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