Knowledge graphs have existed for a long time and have proven valuable across social media sites, cultural heritage institutions, and other enterprises.
A knowledge graph is a collection of relationships between entities defined using a standardized vocabulary.
It structures data in a meaningful way, enabling greater efficiencies and accuracies in retrieving information.
LinkedIn, for example, uses a knowledge graph to structure and interconnect data about its members, jobs, titles, and other entities. It uses its knowledge graph to enhance its recommendation systems, search features, and other products.
Google’s knowledge graph is another well-known knowledge graph that powers knowledge panels and our modern-day search experience.
In recent years, content knowledge graphs, in particular, have become increasingly popular within 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 structured, reusable data layer of the entities on your website, their attributes, and their relationship with other entities on your website and beyond.
In a content knowledge graph, the entities on your website and their relationships can be defined using a standardized vocabulary like Schema.org and expressed as Resource Description Framework (RDF) triples.
RDF triples are represented as “subject-predicate-object” statements, and they illustrate how an entity (subject) is related to another entity or a 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 this in JSON-LD, which allows machines to understand RDF statements about entities.
Image showing how content gets translated into Schema.org using JSON-LD, which forms a connected graph of RDF triples (Image from author, November 2024)
Your website content is filled with entities that are related to each other.
When you use Schema Markup to describe the entities on your site and their relationships to other entities, you essentially express them as RDF triples that form your content knowledge graph.
Sure, we might be simplifying the process a little, as there are a few more steps to creating a content knowledge graph.
But before you start building a content knowledge graph, you should understand why you’re building one 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 shifted from lexical to semantic search. This means less matching of keywords and more matching of relevant entities.
This semantic understanding is even more beneficial in the age of AI-driven search engines like Gemini, SearchGPT, and others.
Your content knowledge graph showcases all the relationships between the entities on your website and across the web, which provides search engines with greater context and understanding of topics and entities mentioned on your website.
You can also connect the entities within your content knowledge graph with known entities found in external authoritative knowledge bases like Wikipedia, Wikidata, and Google’s Knowledge Graph.
This is known as entity linking, and it can add even more context to the entities mentioned on your site, further disambiguating them.
Example of linking an entity to external authoritative knowledge bases using Schema Markup (Image from 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 precise and useful search results for users and qualified traffic for your organization.
Content Knowledge Graphs Can Reduce AI Hallucinations
Beyond SEO, content knowledge graphs are also crucial for improving AI performance. As businesses adopt more AI technologies like AI chatbots, combatting AI hallucination is now a key factor to success.
While large language models (LLMs) can use patterns and probabilities to generate answers, they lack the ability to fact-check, resulting in erroneous or speculative answers.
Content knowledge graphs, on the other hand, are built from reliable data sources like your website, ensuring the credibility and accuracy of the information.
This means that the content knowledge graph you’ve built to drive SEO can also be reused to ground LLMs in structured, verified, domain-specific knowledge, reducing the risk of hallucinations.
A recent research done by data.world has shown that using a knowledge graph of the enterprise SQL database increases accuracy to 54% (from 16%).
Content knowledge graphs are rooted in factual information about entities related to your organization, making them a great data source for content insights.
Content Knowledge Graphs Can Drive Content Strategies
High-quality content is one of the cornerstones of great SEO. However, content marketers are often challenged with figuring out where the gaps are in their existing content about the entities and topics they want to drive traffic for.
Content knowledge graphs have the ability to provide content teams with a holistic view of their entities to get useful insights to inform their content strategy. Let’s dive deeper.