Knowledge Graph: What they are & how they work

Knowledge Graph

What is a Knowledge Graphs?

Knowledge graphs are a logical representation of knowledge that describes the nature of object and the connections between them.

Knowledge graphs are part of “knowledge representation”, a branch in Artificial Intelligence (AI) that is concerned with the presentation of information in a way that allows machines to engage in reasoning, problem solving decisions, inferring, and decision-making.

The range of knowledge graphs is aplenty across different domains, and includes instances that cover:

  • Recommendation engines
  • Advertising that is targeted
  • Analysis of data
  • Semantic search
  • The learning process for Large Language Models (LLMs)

Knowledge graphs enable machines to extract useful knowledge from data, by presenting data in an easily-readable format. But did you realize that you could also design an “content” knowledge graph that is especially useful to SEO initiatives? Though it is structured as a general knowledge graph the content knowledge graph serves as a representation of web pages you have.

The graph could be made available externally to search engines for them to use, or used to support interned AI initiatives, or used to detect gaps in the content. Furthermore, these graphs provide solid foundations for the creation of more comprehensive marketing knowledge graphs, if you have other sources of data you’d like to include in the mix.

Before we dive into it the article will look at the essential elements of knowledge graphs in order to allow you to create your own knowledge graph of content with the content you have on your site.

Research of a Content Knowledge Graph

In its most basic shape the knowledge graph is composed of edges and nodes.

Nodes represent object in a knowledge graph. edges connect these nodes by defining the relationships between them. To understand fully how a knowledge graph operates is to be aware of the technology required to create them.

The purpose of this article is to help you understand the essential terms and the various functions crucial in the creation of a comprehensive Content Knowledge graph.

Uniform Resource Identifier

In the world of knowledge graphs and knowledge graphs, in the realm of knowledge graphs, Uniform Resource Identifier (URI) plays an essential role in identifying specific object. An URI is a unique string of characters created to identify and decode an individual resource on the internet.

Like license plates on automobiles which allow individual identification even though there are the number of people sharing the same brand or model URIs perform a comparable purpose, allowing for the unique identification of resources across the vastness of internet.

In the Schema App, we generate HTTPS URIs for the object you have that are defined by you in the Schema Markup, as shown in the below image. They permit you to connect the elements on your website within your markup. They also allow search engines to find the object on your graph.

This systemic identification allows for efficient communication and accessibility to information across various platforms and technology. In the knowledge graph the URIs are the representation of object .


A term used to describe an entity according to Google defines an individual, distinct identifiable, well-defined, and distinct concept or thing. It is distinguished by its distinctive features or traits like size, colour and length. However, an entity’s real importance is when it’s discussed as a relation the other world object giving it context significance.

This is the reason Resource Description Framework Triples play a crucial role in giving the framework to depict the interconnected relationships of the object of the knowledge graph. What is RDF?

Resource Description Framework

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

Resource Description Framework Triples

The basic element of a knowledge graph is called the triple. It is comprised of two nodes which represent object joined through a single edge, which helps define their connection. Represented as “subject-predicate-object” statements, a triple illustrates how an entity (subject) links to another entity or a simple value (object) through a specific property (predicate).

In the event that these triples mix together, they create connected graphs that contain resources that lay the foundation for a complete knowledge graph. In order to convey an understanding to machines the data, you need to express the triples in a machine-readable format.

It is possible to express Resource Description Framework triples in various formats such as:

  • Turtle
  • And JSON-LD

The most commonly used format is JSON-LD. It’s the one that we employ in our work on Schema App.

JSON for Linking Data

JSON-LD, also known as JSON in the context of Linked Data, is a format used to express serialization of Resource Description Framework triples. It is fairly simple for humans to read and write, and as well for machines to process.

JSON-LD codes allow machines to recognize Resource Description Framework statements regarding object.

For instance, Mark van Berker is an author on Schema App’s blog. Schema App blog, and his author page states he is employed by the company Schema App. The left side you will see Schema Markup, which is Schema Markup expressed in JSON-LD telling computers the computer that Mark van Berker (Person) is employed by Schema App (Organization). On the right side is the same code, rendered in the RDF triple, which depicts the same object as well as illustrating the connections between them.

Descriptive Ontologies

The last element in the knowledge graph is an ontology.

The term “ontology” is used in Information Science, an ontology is an “formal, explicit specification of a shared conceptualization,” in essence, it serves as a guideline for defining the elements that comprise the Data Model.

This type of model typically includes three main components.

The first is that we have classes that are also referred to types, that represent different the different types of object, such as an organization, a particular event or a person.

Second, attributes, or properties are used to define the characteristics of an entity. For example, a person entity could have an identity as an attribute.

Additionally, relationships, that are also represented in properties, define how an entity is connected to another. They are similar to attributes in that they define an entity, however in particular, they define how an particular entity is connected to another.

For instance for instance, for instance, a Person could have an adult, parent or a colleague relationship with a different Person with particular characteristics.

An array of ontologies and vocabularies and glossaries are available for categorizing and linking information including is among the ones most extensively utilized in SEO. Although technically a word, but not a complete ontology, effectively fulfils the purpose of describing the categorizing things and their relationships.

The creation of an Content Knowledge Graph using

In 2011, was founded in 2011 by Google, Bing, Yahoo, and Yandex, emerged as an effort by the three companies to improve the internet by introducing the concept of a standardized vocabulary. The idea was to convert the human tongue into a machine-readable and structured language.

All major search engines should accept this language, which will improve their capability to match queries with relevant results. This is advantageous for SEO.

Although SEO strategies are commonly employed the term, its value extends far beyond its use; it could also function as a solid instrument for creating an Knowledge graph.

Utilizing the vocabulary allows you to arrange your best website content writing services into an interconnected graph. In order to achieve this, you can use the properties and types specified by expressing RDF triples as machine-readable formats such as JSON-LD. You can also represent your entity’s properties using URIs.

Check out how these terms are connected?

This combination of elements creates a knowledge graph of content to help your business.

Make a Content Knowledge Graph for Your Organization

The creation of your own knowledge graph of content is vital to optimizing your SEO strategy for semantics. It prepares your content to be relevant to SEO’s future and brings better quality traffic to your site.

Knowledge graphs allow search engines to discern knowledge from additional context information that bridges gaps to deliver better results. Therefore, this greater understanding should result in more targeted visitors to your website and improve the CTR for pages that are relevant.

We are Schema App, we specialize in the creation and management of knowledge graphs for content using by using Schema Markup. Our flexible authoring solutions guarantee that your Schema Markup is always descriptive updated, interconnected, and current.

If you’re planning to integrate Schema Markup into your SEO strategy, or looking to turn your content into a reuseable data layer Schema App has got your back.

Are you interested in creating a content knowledge graph within your business but aren’t sure how to begin? Schema App handles the technical aspects, allowing users to enjoy the benefits of a well-constructed content knowledge graph, without placing the technical burden on your internal team.