What is a Knowledge Graph?

Data is everywhere, but the real challenge is finding the right data and understanding how it connects. That’s where knowledge graphs come in. By linking related data points, knowledge graphs reveal hidden connections and patterns. For instance, instead of just seeing individual purchases, a knowledge graph connects the dots between a customer’s browsing history, product preferences, past interactions, and even social media activity.

This holistic view helps businesses understand what the customer bought, why they bought it, and what they might be interested in next. With this deeper insight, companies can tailor their offerings, predict future behaviors, and make smarter decisions. By understanding the entire customer journey, businesses gain a clearer view of the bigger picture, enabling more personalized and effective strategies.

In this article, we'll explore knowledge graphs, how they work, and why they've become indispensable tools in many search and data-driven applications, including graph databases, semantic search engines, AI-powered assistants, and complex research systems.

Table of contents

How knowledge graphs are different from traditional databases

Traditional databases treat data as isolated pieces of information, making it harder to see connections. In contrast, a graph representation of data is designed to reveal those connections, providing context that helps you see the bigger picture. By linking related data points, you can uncover insights that traditional databases might overlook, making them invaluable for knowledge management of complex information.

A knowledge graph is a web of interconnected components 

Think of a knowledge graph as a web of connected ideas, much like how your brain associates different pieces of information. If someone mentions "apple," your mind immediately connects it to things like "fruit," "healthy," or "red." A knowledge graph works in a similar way. It links related information that computers can understand and delivers smarter, more relevant search results to users.

For example, if you search for "Leonardo da Vinci" on Google, you're likely to see not just his name but also connections to famous works like the Mona Lisa, his contributions to science, and even related figures from the same era. This isn't magic—it's a knowledge graph at work behind the scenes, organizing related information into a network of connected ideas.

Knowledge graphs help search engines and apps like Netflix and Amazon recommend the best shows, products, or articles just for you. They're also behind artificial intelligence assistants like Siri and Alexa, helping them "understand" your requests and give you more useful responses.

A knowledge graph or database is a semantic web of connected data where each piece integrates information by linking to others meaningfully. Unlike traditional databases storing data in isolated tables, a knowledge graph database connects related information.

Exploring comprehensive search results

Imagine you're a researcher studying climate change. In a traditional database, your search requests would look for individual articles or datasets one by one without knowing how they relate to each other. A knowledge graph database, however, searches for connections between research papers, datasets, scientists, and geographic locations. 

For instance, "CO2 levels" might be linked to "global temperature trends," "arctic ice melt," and specific research papers. This interconnected view lets you quickly identify which studies are most relevant, see the broader context, and even find hidden relationships between different research topics you might have missed with a simple keyword search in a traditional database. 

A knowledge graph enhances your ability to conduct research by linking related articles, data points, and other sources, making it easier to navigate and understand complex topics using semantic networks.

Uncovering deeper connections

To illustrate how a knowledge graph works, imagine a student researching which state offers the most colleges with cybersecurity training. A traditional search might provide isolated facts or lists of schools, but a knowledge graph reveals a deeper web of connections.

For example, "California," "Texas," and "Virginia" could be linked to "cybersecurity training programs" while also displaying the number of programs, the specific schools offering them, and their connections to other industries, like "manufacturing" or "software development."

Instead of just delivering a results list, a knowledge graph helps uncover broader insights. The student might discover that states with solid cybersecurity education also lead to tech job growth, revealing patterns and relationships that a simple search might overlook.

How are connections created?

To explain how knowledge graphs work, let's keep it simple. Imagine a giant map where every piece of information is a dot, and those dots are connected by lines showing how they relate. A traditional database stores data organized only in rigid rows and columns, like a spreadsheet. However, knowledge graphs take a different approach, organizing data to reflect real-world relationships.

Let's go back to the "apple" example. In a knowledge graph, "apple" might be linked to "fruit," "tree," "red," and "healthy," among other things. Each of these links creates a relationship that helps computers "understand" how these concepts fit together. The result? Faster, more accurate responses to your queries.

A closer look: Entities, relationships, attributes, and ontology

To better comprehend how knowledge graphs work, it's helpful to become familiar with their main components: entities, relationships, attributes, and ontology. 

Entities: The building blocks

A knowledge graph consists of entities. Think of an entity as a "thing" that exists in the world—like a person, a company, a product, or an idea. For example, in a knowledge graph, "Leonardo da Vinci" would be an entity, as would "Mona Lisa" and "Renaissance art." Entities are the foundation of the knowledge graph, representing the real world and the entities, objects, or concepts we want to connect.

Relationships: Connecting the dots

The power of a knowledge graph lies in how it shows relationships between entities. Relationships describe how two or more entities are connected. For instance, "Leonardo da Vinci" might have a relationship with "Mona Lisa" as the creator of the painting. These connections make a knowledge graph more than just a list of facts—they show how different pieces of information relate. Relationships are like the threads in a spider web, weaving together different entities to form a knowledge network.

Attributes: Adding detail

While entities and relationships form the structure of a knowledge graph, attributes add details that help describe the entities. An attribute represents a specific piece of information about an entity. For example, the "Leonardo da Vinci" entity might have attributes such as birth date, profession, or nationality. Attributes give each entity more depth and help distinguish one entity from another.

Think of attributes as the extra details that fill in the picture, like the fact that "Leonardo da Vinci" was born in Italy and had many different vocations—scientist, painter, inventor. These details add context and meaning to the description of who he was.

Ontology: The framework of knowledge

Finally, by definition, ontology is the underlying framework that meaningfully organizes entities, properties, relationships, and attributes. In simple terms, ontology defines the rules and categories that help a knowledge graph make sense of the world. It ensures that entities and properties are classified correctly and that the relationships between them are logical.

For example, an ontology schema might define that a "person" can "create" a "work of art" but not the other way around. It provides the structure and rules that guide how knowledge graphs organize and connect information.

Bringing it all together

When you combine entities, relationships, attributes, and ontology, you create a system that stores data and understands how that data is related. This capability allows knowledge graphs to provide deeper insights and more accurate answers. It's not just about finding the meaning of a single piece of information—it's about understanding how that piece fits into the bigger picture.

Real-world knowledge graph applications

Thanks to their ability to organize complex data and uncover connections, knowledge graphs are becoming vital across many industries.

Healthcare

Knowledge graphs can link patient histories, treatment options, and medical research in healthcare, helping doctors make more informed decisions. For instance, a doctor can quickly find relationships between a patient's symptoms, existing treatments, and relevant medical studies to recommend the best course of action.

Finance

Knowledge graphs help analysts identify market trends in finance by linking financial data with broader economic factors like geopolitical events or regulatory changes. By connecting diverse data points, they can uncover information that would likely be missed in traditional data analysis. For instance, an analyst can see how changes in interest rates affect stock prices while accounting for external factors like global trade policies or inflation trends. 

This ability to interlink seemingly unrelated information allows for more accurate forecasting and strategic decision-making, giving financial professionals a competitive edge. Additionally, knowledge graphs streamline the analysis of large amounts of unstructured data, such as news articles and social media feeds, to assess potential real-time market impacts. Integrating real-time data with historical records provides a dynamic tool for predicting market behavior and evaluating investment risks.

Ecommerce and recommendation systems

For e-commerce and recommendation systems, companies like Netflix and Spotify use knowledge graphs to understand user preferences and recommend new content by linking user behavior with related movies, music, or products. 

Building and maintaining a knowledge graph

Creating a knowledge graph involves several key steps:

Data collection: Gather data from various sources, including structured data (e.g., databases), unstructured data (e.g., documents, articles), and real-time information streams.

Data cleaning and standardization: Clean, format, and standardize the collected data to ensure consistency across the knowledge graph.

Organizing data: Structure the data into entities (nodes) and relationships (edges), representing the connections between these entities.

Developing the ontology: Define the rules (ontology) that categorize and connect graph entities and relationships. For instance, in a retail knowledge graph, the ontology would ensure that "products" are linked to "manufacturers" and "customer reviews."

Maintaining the knowledge graph: Regularly update the knowledge graph as new data emerges. Automating this process with machine learning can help identify new relationships and expand the graph.

Managing growth: As the graph grows, ensure data accuracy, manage its complexity, and address scalability challenges with the help of data engineers and domain experts.

By following these steps, a well-maintained knowledge graph database can reveal insights and connections that traditional databases might overlook.

Challenges and limitations of knowledge graphs

While knowledge graphs are powerful tools, they come with significant challenges. Building and maintaining an effective knowledge graph can be resource-intensive, requiring continuous input from data engineers, domain experts, and machine learning models to keep the graph relevant and accurate. Scalability is another issue—large-scale graphs with millions of entities and relationships can become complex to manage and query efficiently. Additionally, creating a well-structured ontology, which defines how data points relate to each other, is essential but difficult; without it, the graph can become disorganized or even misleading.

Data quality is also critical. Poor or inconsistent data can lead to incorrect connections or faulty insights, undermining the graph’s effectiveness. Addressing these limitations requires ongoing investment in technology, a strong commitment to data governance, and a clear strategy to ensure the graph remains scalable and accurate as it grows.

The future of knowledge graphs

As tech keeps evolving, knowledge graphs are set to play a more significant role in AI and other machine learning algorithms. They’ll help make systems smarter, allowing them to better understand and work with complex data. Imagine AI using knowledge graphs to spot patterns in scientific research or offer personalized healthcare advice. With knowledge graphs becoming more connected to other data systems, their usefulness will grow. In the near future, businesses will lean on them to manage complex data and push innovation in areas like self-driving cars, smart cities, and advanced medicine—all thanks to their flexibility and scalability.

FAQ

Can knowledge graphs be integrated with other systems?

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Knowledge graphs are flexible and can be integrated with other data integration tools, systems, and platforms. They can connect to traditional databases, data lakes, and machine learning models, allowing organizations to leverage their existing data infrastructure while gaining the benefits of a new knowledge graph data model. By integrating knowledge graphs with other tools, businesses can enrich their data, enhance analytics, and improve decision-making processes across multiple departments or applications.