How To Leverage Your Content Knowledge Graph To Support Your Marketing Strategy

Knowledge graphs have been around for a long time and have proven valuable to social media sites, heritage institutions and other businesses.

AND knowledge graph is a collection of relationships between entities defined using a standardized vocabulary.

Structures data in a meaningful way, enabling greater efficiency and accuracy in information retrieval.

LinkedInfor example, it uses a knowledge graph to structure and interconnect data about its members, jobs, titles, and other entities. It uses its knowledge graph to improve its recommendation systems, search features and other products.

Google Knowledge Graph is another well-known knowledge graph that powers knowledge boards and our modern search experience.

In recent years, content knowledge graphs in particular have become increasingly popular in the marketing industry due to the rise of semantic SEO and AI-driven search experiences.

What is a Content Knowledge Graph?

A content knowledge graph is a specialized type of knowledge graph.

It is a reusable structured data layer entities on your website, their attributes and their relationship with other entities on and off your website.

In a content knowledge graph, the entities on your website and their relationships can be defined using a standardized vocabulary such as Schema.org and expressed as a Triple Resource Description Framework (RDF).

RDF is tripled they are represented as “subject-predicate-object” statements and illustrate how an entity (subject) is related to another entity or simple value (object) through a specific property (predicate).

For example, I, Martha van Berkel, work for Schema App. This is stated in plain text on our website, and we can use Schema.org to express it in JSON-LD, which allows machines to understand RDF entity statements.

Image showing how content is translated into Schema.org using JSON-LD, which forms a connected graph of RDF triples (image by author, November 2024)

The content of your website is filled with entities that are interconnected.

When you use SchemaMarkup to describe the entities on your page and their relationships to other entities, you essentially express them as RDF triples that form your content knowledge graph.

Of course, we may be simplifying the process a bit because there are a few more steps creating a content knowledge graph.

But before you start building a content knowledge graph, you should understand why you’re building it and how your team can benefit from it.

Content knowledge graphs drive semantic understanding for search engines

Over the past few years, search engines have moved from lexical to semantic search. This means fewer keyword matches and more relevant entity matches.

This semantic understanding is even more useful in the age Artificial intelligence driven search engines like Gemini, SearchGPTand others.

Your Content Knowledge Graph shows all the relationships between entities on your website and across the web, giving search engines better context and understanding of the topics and entities mentioned on your website.

You can also link entities within your Content Knowledge Graph to known entities found in external authoritative knowledge bases such as Wikipedia, Wikidata, and Google’s Knowledge Graph.

This is known as entity linking and can add even more context to the entities mentioned on your website, further clarifying them.

An example of linking entities to external authoritative knowledge bases using a schema tag (Photo by author, November 2024)

Your content knowledge graph ultimately enables search engines to explicitly understand the relevance of your content to a user’s search query, leading to more accurate and useful search results for users and qualified traffic for your organization.

Content knowledge graphs can reduce AI hallucinations

In addition to SEO, content knowledge graphs are also key to improving AI performance. As businesses adopt more and more AI technologies like AI chatbotsfighting AI hallucinations is now a key success factor.

While large language models (LLM) they can use patterns and probabilities to generate answers, they lack fact-checking ability, resulting in wrong or speculative answers.

Content Knowledge Graphs, on the other hand, are built from trusted data sources like your website, ensuring the credibility and accuracy of the information.

This means that the content knowledge graph you built to drive SEO can also be reused to ground the LLM in structured, validated domain-specific knowledge, reducing the risk of hallucinations.

Content Knowledge Graphs are based on factual information about the entities associated with your organization, making them an excellent data source for content insights.

Content knowledge graphs can drive content strategies

High quality content is one of the cornerstones of excellent SEO. However, content marketers often face the challenge of identifying where the gaps are in their existing content about the entities and topics they want to drive traffic to.

Content Knowledge Graphs have the ability to provide content teams with a holistic view of their entities to gain actionable insights to inform their content strategy. Let’s dive deeper.

Get a holistic view of the entities in your content

Traditionally, content marketing teams would manually audit or use a spreadsheet or relational database (tables, rows, and columns) to manage their content. The problem with a relational database is its lack of semantic meaning.

For example, the table might include the title, URL, author, meta description, word count, and topic of the article. However, it cannot include the entities mentioned in the article in plain text.

If you want to know which pages on your website currently mention an old product that you no longer offer, identifying those pages is difficult and very manual.

Content Knowledge Graphs, on the other hand, provide a multi-dimensional categorization system for your content.

When built using Schema.org Dictionarydetailed types and properties allow you to capture relationships between different pieces of content based on entities and taxonomy.

For example, a blog post on your website would likely appear on your content knowledge graph as Posting on a blog with properties like author, publisher, mentions, publication date, dateModified, audience, readand more.

These properties link your blog article (entity) to other entities you’ve defined on your website. The author of a particular article is the person you may have defined on the author page.

Your article might mention a product or service you’ve defined on other pages of your site.

An example of a content knowledge graph showing how a blog post is related to other entities via Schema.org properties (Image by author, November 2024)

For marketing teams that must manage large amounts of content, structuring your content into a content knowledge graph can give you a more holistic view of your content and entities.

You can easily perform a content revision to find out what’s on your website without manually auditing the page or updating a spreadsheet.

This in turn allows you to easily perform content analysis and gain deeper insight into your content.

Get deeper insight into your content

With the holistic view provided by your Content Knowledge Graph, you can easily audit your content and entities to identify gaps and opportunities to improve your content strategy.

Example 1: You want to strengthen yours EAT for specific authors on your site. Your Content Knowledge Graph will show:

• All content created, edited or contributed to by this author.
• How the author is related to your organization and other recognized entities.
• Author role, job title, awards, credentials and certifications.

This consolidated view can provide your team with a broad overview of this author and identify content opportunities to improve the author’s authority on your website.

Example 2: Your organization would like to remove all references to the COVID-19 protocol from your website.

You can query your content knowledge graph to identify past content that mentions the topic “COVID-19” and assess the relevance and necessity of each mention before removing it from your content.

This targeted approach can allow your team to improve their content without investing too much time in manual reviews.

Because content knowledge graphs built using Schema.org are expressed as RDF triples, you can use the SPARQL query language to find out which pages mention a particular entity or how much content you have about a particular entity or topic.

This will help your team answer strategic questions such as:

• Which entities are not represented in the content of your website?
• Where can additional content be created to improve entity coverage?
• What existing content needs improvement?

Beyond SEO and AI benefits, content knowledge graphs have the potential to help content marketing teams perform content analysis with greater efficiency and accuracy.

It’s time to start investing in Content Knowledge Graphs

Today, content knowledge graphs represent a shift from thinking of content creation as a content manager’s job to an opportunity for SEO professionals to create an interconnected source of content data that answers questions and identifies opportunities for the content team.

It is a key technology for organizations looking to differentiate themselves in an increasingly complex digital landscape.

Investing in Content Knowledge Graphs now puts your organization at the forefront of SEO and content optimization, giving you the tools to navigate the challenges of tomorrow.

And it all starts with the implementation of semantic schema markup on your page.

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