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In-depth Guide to Knowledge Graph: Benefits, Use Cases & Examples

Updated on Feb 14
5 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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The rapid growth of data in today’s digital world has made data governance a challenging task. According to McKinsey, even the global leading firms can waste between 5-10% of employee time on non-value-added tasks due to poor data governance.1 This can rise up to %29 in average across enterprises.

Knowledge graphs have emerged as a powerful solution to address these issues. A knowledge graph integrates data from diverse sources into a unified, structured, and interconnected representation, offering a more comprehensive view of information. 

By doing so, knowledge graphs not only streamline data governance but also unlock numerous benefits and use cases across various domains. In this article, we explain what a knowledge graph is with its benefits, use cases and real life examples.

What is a knowledge graph?

Figure 1. A simple knowledge graph representation

Source: Wikipedia

A knowledge graph is a structured representation of information and knowledge in the form of a graph. It consists of nodes (real world entities or concepts) connected by edges (relationships or associations). The purpose of a knowledge graph is to model, store, and organize complex information in a way that makes it easy for both humans and machines to understand, navigate, and use the knowledge it contains.

Powered by machine learning algorithms, knowledge graphs employ natural language processing (NLP) to create an extensive representation of nodes, edges, and labels via a technique known as semantic enrichment. As data is ingested, this method enables knowledge graphs to recognize distinct entities and comprehend the connections between various entities.

How does a knowledge graph work?

Figure 2. Steps involved in the construction of a knowledge graph

Source: ResearchGate

Creating a knowledge graph involves several steps that help in organizing and representing the information effectively:

  1. Data collection: Gathering the underlying data from various sources like databases, websites, or documents.
  2. Entity identification: Recognizing and distinguishing entities (people, places, etc.) in the collected data model.
  3. Relationship extraction: Determining the connections between the identified entities.
  4. Ontology creation: Developing a well-defined structure (ontology) to organize the entities and their relationships.
  5. Data storage: Storing the knowledge graph in a specialized database designed for handling graph data.
  6. Querying: Using graph query languages to search, navigate, and explore the connections in the network.
  7. Inference: Performing advanced tasks like discovering new relationships or identifying inconsistencies within the graph.

What are the benefits of knowledge graphs?

Knowledge graphs offer several general benefits that apply across various applications and industries. Some key general benefits include:

1- Data integration

Knowledge graphs can help link and harmonize data from diverse sources, fostering data sharing and collaboration among organizations, and allowing for a more comprehensive view of the information.

2- Enhanced understanding

Knowledge graphs provide a richer context and understanding of information by representing entities and their relationships, enabling both humans and machines to better interpret and interact with the data.

3- Flexibility

Knowledge graphs can be tailored to various domains and industries, allowing for a wide range of applications and customization to meet specific needs.

4- Improved search and discovery

By modeling relationships between entities, knowledge graphs can deliver more relevant, accurate, and comprehensive search results and facilitate the discovery of new knowledge and insights.

5- Inference and reasoning

Knowledge graphs support various inference and reasoning tasks, enabling the discovery of new relationships, identification of inconsistencies, and validation of existing knowledge.

6- Structured representation

Knowledge graphs provide a structured way of organizing and representing information, making it more accessible and easier to work with, especially for artificial intelligence and machine learning applications.

7- Scalability

Knowledge graphs are efficient at representing and storing large amounts of interconnected information, making them suitable for handling vast datasets and large-scale applications such as data science.

What are the applications of knowledge graphs?

By understanding the context and relationships between entities via the semantic web, knowledge graphs can enhance search engine results, offering more relevant and comprehensive information to users.

2- Question answering

Knowledge graphs can facilitate answering questions by identifying relevant information and connections within the graph, making them valuable for virtual assistants and chatbots.

Figure 3. The effect of knowledge graph on chatbot systems

Source: Onlim

3- Recommendation systems

By understanding user preferences, interests, and behavior, knowledge graphs can provide personalized recommendations in areas such as e-commerce, content discovery, and entertainment.

Knowledge graphs are utilized in AI-driven recommendation systems for content platforms, such as Netflix, SEO, or social media. By analyzing users’ clicks and other online engagement activities, these platforms can suggest new content for users to read or view.

Check out our article on recommendation systems to learn more.

4- Natural language processing

The relationship between knowledge graphs and NLP can be described as a mutually beneficial interaction. This relationship leads to more effective language processing and a better understanding of the connections and context within the data.

Knowledge graphs provide structured information about entities and their relationships, which helps NLP systems better understand and process textual data. On the other hand, NLP techniques are used to extract entities and relationships from unstructured text, contributing to the creation and expansion of knowledge graphs.

By incorporating background knowledge and context from knowledge graphs, NLP models can perform more effectively and accurately across a range of tasks such as:

Figure 4. Knowledge graphs are used as semantic triples for NLP

Source: Accenture

5- Enterprise knowledge management

In businesses and organizations, an enterprise knowledge graph can help capture, store, and organize knowledge, improving the accessibility of information for employees through data management.

6- Biomedical research

Knowledge graphs are employed to represent complex relationships between genes, proteins, diseases, and drugs in biomedical fields. So, they facilitate the discovery of new insights and aid drug development.

If you are interested in the use of high tech in this field, you can check our article on the use of generative AI in life sciences.

Figure 5. An example of a constructed biomedical knowledge graph

Source: ResearchGate

7- Fraud detection and security

Knowledge graphs can help identify unusual behavior or connections by modeling relationships and patterns within large datasets of transactions and organizations.

By analyzing these relationships and patterns in the knowledge graph, it becomes easier to spot potential fraud or security threats, such as: 

  • Suspicious transactions
  • Fake accounts
  • Abnormal user behavior

Knowledge graphs can be an effective tool for detecting and preventing fraud and enhancing overall security in various industries, including finance, e-commerce, and social networks.

What are some examples of knowledge graphs?

1- DBpedia

DBpedia is a large-scale, open-source knowledge base derived from the structured information available in Wikipedia. It represents information as a knowledge graph.

It aims to make Wikipedia’s content more accessible, machine-readable, and useful for various applications and research purposes. Launched in 2007, DBpedia knowledge graph acquires structured data from Wikipedia infoboxes, categories, links, and other elements, converting it into a standardized format by DBpedia ontology.

Figure 7. DBpedia ontology overview with classes and instances in each class

Source: DBpedia

In addition to being used for research and academic purposes, DBpedia also serves as a foundational resource for various applications, including search engines, recommendation systems, and natural language processing tools.

2- Google Knowledge Graph

Launched in 2012, Google’s Knowledge Graph is designed to provide users with more relevant, contextual, and informative search results by understanding the relationships between different entities. 

It helps power Google search, Google Assistant, and various other Google services. When users search for a topic or entity, the Google Knowledge Graph can display a knowledge panel alongside the search results, providing quick access to key information, facts, and related entities (Figure 6). This not only improves the overall search experience but also helps users explore and discover new information more efficiently.

Figure 6. Google knowledge panel

3- Microsoft Satori

Microsoft Satori is a knowledge graph developed by Microsoft that powers various applications and services, such as the Bing search engine and the Cortana virtual assistant. Similar to Google’s Knowledge Graph, Satori aims to provide a comprehensive understanding of entities, their attributes, and the relationships between them in order to enhance search results.

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Cem Dilmegani
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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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