Knowledge Graph

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The term Knowledge Graph gained popularity with the launch of the Google Knowledge Graph in 2012 and the introduction of similar large databases by other major tech companies, such as Yahoo, Microsoft, and Facebook. These knowledge graphs enable semantic searches and smarter data processing of machine learning tasks.

What is a Knowledge Graph

A knowledge graph is a structured representation of interconnected descriptions of real-world entities found in a knowledge base. It utilizes linking and semantic metadata to provide a framework for data integration, unification, analytics, and sharing.


Semantic network
Conceptual network

How a Knowledge Graph Works

Combining schemas, identities, and context seamlessly provides the graph structure.

  • Schemas establish the framework for the knowledge graph, defining relationships and attributes between different entities.
  • Identities help classify nodes accurately by giving them specific labels that determine their role within the graph.
  • Context sets the stage for the information, providing a deeper understanding of how different pieces of data relate to each other.

Knowledge graphs can disambiguate words with multiple meanings through these types of entities, ensuring that the right interpretation applies in each instance.

One practical application of knowledge graph technology is Google’s search engine algorithm, which relies heavily on this method to improve user search results by understanding the context of queries. For example, when a user searches for “Apple,” Google can differentiate between results related to the tech company Apple Inc. or information about the fruit itself based on identifiers within its knowledge graph. Machine learning techniques are employed in conjunction with natural language processing (NLP) tools to enhance knowledge graphs through semantic enrichment, creating a comprehensive view of nodes interconnected by edges and labeled with relevant information. Overall, knowledge graphs play a

Building a Knowledge Graph

The knowledge database serves as the foundation for data aggregation from various sources while maintaining the semantic structure through an ontology or schema. This structured approach enables the system to accurately capture graph-shaped data and organize it into entities, relations, and attributes.

Organized data is then applied deductive logic to infer new knowledge that may not have been initially obvious. By modeling the real world in such a way, a knowledge graph can act as a bridge between humans and computers, facilitating easier access to complex information.

The ability of a knowledge graph to maintain context and semantics from heterogeneous data sources is what sets it apart from traditional databases. By preserving this information as it is ingested into the system, the knowledge graph provides a more accurate representation of the real world. 

Knowledge graphs improve search experiences by providing relevant and contextualized information to search queries. They do this by collecting and organizing data from various sources and building relationships between different entities. These relationships are based on pattern analysis and understanding the context. Search results become increasingly accurate and relevant as users interact with knowledge graphs.

Why Do Knowledge Graphs Matter?

The purpose of Google’s Knowledge Panel is to help users find the right thing and understand intent. Additionally, they wanted to use graphs to summarize relevant content.

Using summarization of sources like Wikipedia pages the algorithms also want to provide deeper insights into queries, which you can see in the knowledge panels today, where you can click deeper into content, maybe discover things you didn’t know about, discover connections you weren’t aware of, or narrow down your search to really get to the original intent of why you started doing that search.

Google’s Knowledge Graph presents us with a ton of ranking potential in the Knowledge Panel and other places throughout the SERPs. It allows us to have a deeper understanding of how our users interact and how search engines understand our queries. We can use this information to create targeted content and content that matches intent. But we can also use this to build a Knowledge Graph of our own to enrich our sites for both our users and the search engines.

The Advantage of Knowledge Graphs

Much like Google uses its knowledge graph as the engine to power up its search results, you can create a knowledge graph that describes the content of your site in a way that helps machines understand what it’s really about. We add context about the relationship between entities so our web context connects to the right content. 

In the simplest form, you can build a knowledge graph by doing the following: defining the entities and the concepts that you or your site is about, creating pages with unique URLs for each of those, and then beginning to annotate your content to connect them all together, to start building these relationships.


Where Do Knowledge Graph Facts Come From?

Information in the Knowledge Graph comes from various sources, including public databases, licensed data, and direct submissions from content owners.

How Does Google Correct or Remove Knowledge Graph Information?

With billions of searches being processed daily, automation is essential in handling such a vast amount of data. Google’s automated systems are designed to prevent the display of any information that violates their policies, but due to the sheer scale of search queries, no system can be perfect. To address this issue, Google provides public reporting systems where users can flag inaccurate or inappropriate content. This data is carefully analyzed and used to make adjustments to improve the algorithms that power Google’s search results.

By leveraging user feedback through its public reporting systems, Google can continually refine and enhance its search algorithms. Removing incorrect or misleading information from the Knowledge Graph improves user experience and helps uphold Google’s standards and policies. The insights gained from analyzing user-reported data allow Google to make informed decisions on filtering out harmful content better and ensure that accurate and trustworthy information is delivered to users. This ongoing process underscores Google’s commitment to providing reliable search results and maintaining high quality in its services.

How Are Knowledge Graphs Used in Machine Learning?

By feeding Knowledge Graphs to machine learning algorithms, we can leverage this rich data source to train models with a deeper understanding of the world around them.

The semantic nature of Knowledge Graphs makes them ideal training data for machine learning algorithms. With clear connections and labels between entities, machines can learn the underlying patterns and structures within the data more effectively. This allows for more accurate predictions and insights to be made, leading to better decision-making processes in various applications such as natural language processing, recommendation systems, and search engines. Overall, Knowledge Graphs play a crucial role in advancing the capabilities of machine learning by providing a comprehensive and structured representation of knowledge that helps machines make sense of complex information.

What Is the Difference Between an Ontology and a Knowledge Graph?

A knowledge graph is a structured form of data organized based on ontologies. The most commonly used ontology is, which Google has adopted primarily for its Google Knowledge Graph.

Ready to build your first knowledge graph? I’ve created this course to walk you through it step-by-step. We have a generator and multiple ways for you to add it to your site and ensure that you get the results you’re looking for. Get access to the course today at

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