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December 12, 2023

Knowledge Graphs: Transforming Data into Valuable Insights


Artur Haponik

CEO & Co-Founder

Reading time:

13 minutes

In the era of information overload, the ability to extract valuable and actionable insights from large amounts of data has become increasingly important. Unfortunately, many organizations are struggling with the challenge of extracting these insights to support informed decision-making. This is where knowledge graphs (KGs) come in.

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Knowledge graphs are powerful representations in data science that offer a transformative approach to organizing, managing, and analyzing information. By representing data as interconnected concepts, entities, events, and relationships, knowledge graphs provide a comprehensive view of information, enabling organizations to uncover insights, patterns, and trends previously obscured by the noise of large datasets. This has made it easier for organizations around the world to capitalize on emerging trends, make informed decisions, drive innovation, and stay ahead of the competition.

This post will provide an in-depth review of what knowledge graphs are and their fundamental concepts, how they work, their real-world applications, and how they support Artificial Intelligence (AI) tools in fostering a deeper understanding of raw data.

What is a knowledge graph? Definition

They are also known as semantic networks; they are structured representations of knowledge that relies on a graph-based data model to organize and integrate information from multiple data sources, capture information about various entities of interest, and forge relationships between these entities. Generally, KGs are made up of three main categories, namely nodes, edges, and labels.

knowledge graph infographic


Nodes usually represent entities such as a person, object, place, concept, or idea. On the other hand, an edge usually denotes the relationship between the entities. For example, if you’re dealing with business information, a node can be an employee, organization, customer/client, agency, or place. An edge will then be used to represent the relationship between the various listed nodes/entities, thus providing context and meaning to the data.

A knowledge graph basically serves as the structure in which an application stores its information. This information is usually added to the graph through a combination of human, automated, and semi-automated methods. Regardless of the method used, the overall expectation is that the information can easily be interpreted, understood, and even verified by humans.

Notably, knowledge graphs store knowledge in triplets using the Subject-Predicate-Object (SPO) Triple format, aligning with Resource Description Framework (RDF) standards. [1] Generally, the existence of a given SPO triplet means that a certain type of relationship exists between the three entities/triplets. The SPO triple format is commonly used across knowledge representation, data science, natural language processing, and machine learning because they’re easy to understand and can be efficiently stored by computers.

In recent times, KGs have played a vital role in representing the information gathered using Natural Language Processing (NLP) and computer vision. [2] Nowadays, data professionals input domain knowledge represented in knowledge graphs into machine learning models to generate better predictions, recommendations, and classifications.

The ability of a KG to highlight the most complex underlying relationships between data and entities allows for a more intuitive and interconnected representation of knowledge. Additionally, they rely on machine learning technology to enable better storage, retrieval, and inference of information, thus facilitating advanced reasoning tasks and search engine results.

Other graphs, like Google knowledge graph, utilize JSON API and markup to generate schematic data that makes it possible for website content to appear on the Search Engine Results Page (SERP). [3]

In data modeling, many prefer to use graphs because they offer a pleasant, adaptable, and flexible way of effectively representing complex relationships and interconnectedness within large amounts of data. However, unlike other graphs, knowledge graphs have an organizing principle that makes it easier for humans and software to interpret data quickly. Therefore, instead of repeatedly trying to improve the intelligence of applications, such graphs encode intelligent behavior into the data only once.

Although KGs result from many years of semantic computational research, the arrival of modern graph computations means that KGs can now easily be used to tackle real-world problems.

Read more about AI Knowledge Management: How AI is Used in Knowledge Management

Examples of knowledge graphs

Currently, there are several types of graphs developed by different companies for different purposes. Some small graphs have been primarily created to define user expectations for search systems within the organizations that created them. On the other hand, there are bigger knowledge graphs openly available to the public and used by millions of people to perform various online functions worldwide.

The following are the most common examples of big knowledge graphs:


Google announced its knowledge graph back in 2012 as a way to significantly improve the quality of search results returned by the company’s search engine. Google helped increase the interest of various academic and business communities in representing knowledge in the form of graphs, thus leading to the coinage of the term ‘knowledge graph’. Google’s graph was initially available only in English but has since been expanded to other languages such as Spanish, French, German, Russian, Japanese, and Italian.

Google’s graph is usually represented through Searching Engine Results Pages (SERPs) that provide information to millions of people around the world based on what they search. This graph comprises over 500 million objects sourced from Wikipedia, the CIA World Factbook, Freebase, and many other sources. The comprehensive nature of this graph makes it particularly useful among students and researchers looking to conduct extensive research on various topics.

Unfortunately, there is limited information regarding Google’s knowledge graph’s organization, size, and coverage. There are also limited means of using this graph outside Google’s devices and projects.


DBpedia’s knowledge graph leverages structured data from Wikipedia’s infoboxes. Its graph ontology mainly comprises entities such as people, organizations, books, films, species, places, creative works, diseases, and many others.

What is DBpedia, and why is it important?


It’s important to note that DBpedia’s graph is the backbone of the LinkedIn Open Data (LOD) movement and has helped many organizations create their own knowledge graphs with millions of crowdsourced entries. Most importantly, both employers and job seekers can use this graph to their benefit.

For example, employers can use DBpedia to improve job descriptions by adding important details such as skills required, related occupations, and other information related to the respective industry. Job seekers, on the other hand, can use DBpedia to search for available job openings that match their qualifications and skills or those located in a specific area.


Wikidata is a free knowledge graph created by the Wikimedia Foundation. Unlike DBPedia, which comprises data from Wikipedia’s infoboxes, Wikidata mainly focuses on secondary and tertiary objects.

This multilingual and collaboratively edited knowledge graph contains millions of facts and statements that Wikipedia users and other entities can use under the Creative Commons (CC0) public domain license. [4] People worldwide can also use Wikidata to perform background checks on various individuals. Most importantly, the Wikidata serves as the primary source for various small graphs and allows anyone around the globe to contribute and improve the published information.

Here are some reasons why Wikidata is one of the most popular knowledge graphs out there:

  • It is one of the largest knowledge graphs available in the world today.
  • Despite being manually curated; the overall cost of curation is usually shared among a community of contributors.
  • Wikidata editorial policies stipulate that all the data available on the graph must be easily understandable and verifiable.
  • The graph makes a great effort to provide users with semantic definitions of all the different relation names through the vocabulary found in [5]
  • Despite having several use cases, Wikidata’s primary use case is to improve web search across search engines.


WordNet is undoubtedly one of the most popular and comprehensive knowledge graphs for the English language. This graph provides users with definitions, synonyms, antonyms, and hypernyms in more than 200 languages to help them understand the semantic relationships between various words.

WordNet’s graph is often used to enhance the efficiency of natural language processing and search applications. Anyone interested in gaining a deeper and more comprehensive understanding of the English language can use this graph for their personal needs.


GeoNames is an open and free geographical knowledge graph that provides its users with easy access to comprehensive information about geographical entities such as countries, cities, towns, mountains, hills, lakes, and rivers. This graph is commonly used for location-based searches, geographical analysis, and various mapping applications. The United States is the most covered country in GeoNames, followed by China, India, Norway, Mexico, and Russia. [6]


FactForge is a popular knowledge graph that provides consumers of Linked Open Data with news articles about people, organizations, and places. This graph contains over 1 billion facts from other popular graphs such as DBpedia, WordNet, GeoNames, and the Panama Papers. It also comprises ontologies like the Financial Industry Business Ontology (FIBO) and live stream news articles linking world news to various entities, concepts, and ideas. [7]

In addition to providing users with thousands of news articles daily, FactForge contains sample queries about their unique capabilities in the media monitoring of interconnected entities and analysis of emerging industry trends.

Knowledge graphs in text analysis

They have become increasingly important in enhancing the accuracy and effectiveness of text analysis tasks, such as relationship extraction, sentiment analysis, and text summarization.

Here are the various ways in which knowledge graphs are used in text analysis:

  • The interconnectedness and relationships between different concepts and entities in graphs can be used to add context and ensure a more accurate interpretation of the meaning and intent of the text at hand.
  • Linking textual content to concepts and entities in a graph helps provide better search results and further analytics of the available data.
  • Representing underlying relationships between entities in a knowledge graph facilitates the extraction of complex relationships present in the text. This is vital for text analysis tasks like sentiment analysis and opinion mining.
  • The data stored in KGs can be used as input data for text analysis tasks, thus improving them significantly.
  • Facts extracted from textual data can be added to enrich graphs, making them better suited for data analysis, reporting, and visualization.

Purpose of knowledge graphs

They have several purposes across a wide variety of industries. These purposes include:

Responsive and contextually-aware content

Knowledge graphs enable recommendation systems to consider various factors and identify hidden patterns between them when generating content recommendations. By simply considering contextual factors, such as user interests, clicks, likes, user location, and online engagement behaviors, these recommendation systems can easily deliver more relevant and personalized recommendations to users. [8]

Nowadays, content platforms such as Netflix, YouTube, Amazon, Facebook, and Spotify use predictive graphs as the basis for their AI-based recommendation systems.

Fraud detection and prevention

In the finance and banking industry, knowledge graphs have been used to create Know Your Customer (KYC) and anti-money laundering guidelines. These graphs usually provide financial institutions with a structured framework for integrating different types of data, such as customer information, financial transactions, and social media activity. This allows for an extensive analysis of the underlying relationships between different entities and easier identification of instances of fraud.

Additionally, graphs can be combined with machine learning techniques to help develop advanced fraud detection and prevention systems. These systems will be able to learn from historical data and adapt to new financial fraud trends, thus improving their overall performance and effectiveness.

Semantic search

Knowledge graphs support semantic search and navigation by helping search engine systems understand the meaning, context, relationships, and intent behind search terms rather than relying only on keywords. This leads to more accurate search results and improved user experiences.

In addition to returning the most relevant results, knowledge graphs uncover all hidden layers of connections between search terms to ensure no relevant results are left behind.

Drug discovery

Over the years, those graphs have proven essential in the healthcare industry. Medical providers often use these graphs to identify valuable insights and hidden relationships between genes, proteins, diseases, drugs, and other biological entities. Unifying these insights with disease models and scientific research helps accelerate the process of drug discovery.

Knowledge graphs and artificial intelligence

Although graphs and Artificial Intelligence (AI) are two different concepts, experts report that their combination is bound to result in advancements in the AI space. That said, here are the different ways in which KGs contribute to AI:

Machine learning enhancement

In the real world, most of the available data is usually unstructured. Although KGs can easily store and interpret such data, there is a huge risk in using unstructured data for machine learning tasks. Fortunately, KGs are capable of transforming unstructured data into a structured and machine-readable format using techniques like text mining and information extraction.

By doing so, AI and machine learning models can be trained on structured and semantically rich data generated by the graphs. This helps improve the learning capabilities of these models and their ability to understand complex relationships.

Data integration

As aforementioned, KGs can easily integrate and utilize data from different sources, providing a unified and structured representation of the data. Organizations can then utilize the unified data to develop advanced AI systems that can operate on large and complex datasets. [9]

Such systems help organizations improve operational efficiency through task automation and make informed decisions.

Final thoughts

The above guide shows that a knowledge graph is an ideal tool for those looking to transform raw data into valuable and actionable insights. By leveraging the connections and underlying relationships between different data points, knowledge graphs allow organizations to interpret and understand data much better.

Thanks to this interconnected approach, organizations can enjoy many benefits, including improved data integration, AI readiness, and better decision-making. This helps pave the way for a more informed, innovative, and insightful future. Contact us today for unparalleled innovation and strategic success through our specialized AI consulting services


[1] RDF. URL: Accessed on December 8, 2023
[2] Computer Vision. URL: Accessed on December 8, 2023
[3] SERP. URL: Accessed on December 8, 2023
[4] Public Domain. URL: Accessed on December 8, 2023
[5] URL: Accessed on December 8, 2023
[6] Places and their names — observations from 11 million place names. URL: Accessed on December 8, 2023
[7] Openriskmanual. Org. Financial Industry Business Ontology.  URL: Accessed on December 8, 2023
[8] Benefits of Personalized Content Recommendations. URL: Accessed on December 8, 2023
[9] What is Unified Data: Definition, Importance, and Unification Process. URL: Accessed on December 8, 2023


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