Knowledge Graphs: Unlocking the Value of Data

This month’s blog is dedicated to what you might call a quiet revolution in artificial intelligence (AI): Knowledge graphs. Compared to today’s hot topics like large language models (LLMs), generative AI (GenAI), and neural networks, this is a technology that receives scant attention. Nevertheless, knowledge graphs feature in Gartner’s 2025 hype cycle for AI, where they’re scheduled to reach their “plateau of productivity” in the coming 2 to 5 years.

One key driver behind the uptake of this tech is its ability to deliver an “external source of truth”, helping identify GenAI hallucinations before they cause issues. In a time when a staggering 90% of the world’s data has been created in just the past two years, knowledge graphs can also help mitigate the risk of data fragmentation within enterprises. Let’s dig a little deeper into this important but often overlooked technology.

Joining the Dots in A Fragmented Data Landscape

While the tech is nothing new – Google introduced its own knowledge graphs way back in 2012 – it’s not nearly as widely known as the other AI topics mentioned above. So, what exactly do knowledge graphs involve, and how do they work?

A knowledge graph can be seen as a kind of data map that organizes all your information. Just as geographical maps show the roads linking cities and towns, so knowledge graphs provide connections between related elements.

In more technical terms: Unlike traditional databases, which store data in isolated rows and columns, knowledge graphs focus on meaning and context. This achieved by using a data structure that connects information in the form of nodes (entities) via edges (relationships between those entities).

Knowledge Graphs in Action

The following example highlights this difference between databases and knowledge graphs. Instead of simply storing the isolated term “Paris” in a table, knowledge graphs leverage a technology known as Knowledge Discovery in Databases to extract a range of relevant information such as the following:

• Paris is the capital of France.

• It has a population of 2.1M.

• It is twinned with Rome, and

• It hosted the 2024 Olympics.

A knowledge graph connects and contextualizes these facts so that AI systems can reason using them. In this way, it helps enterprises extract insights from vast numbers of unstructured documents, bridging the gap between data and understanding.

The Pivotal Role of Knowledge Discovery

Knowledge Discovery in Databases (KDD) is much more than an ancillary technology; it’s the foundation on which knowledge graphs are built. KDD is essentially a systematic approach to discovering valuable patterns, relationships, and insights from large structured or unstructured datasets. It delivers the input for knowledge graphs by identifying entities and relationships from text, logs, images, or sensor data, and integrating them into the graph.

A Whole Host of Benefits for AI and More

So, what does all this add up to for organizations? For one thing, the tech enables AI explainability by making data relationships more interpretable. This opens the door to context-aware search and AI reasoning that goes far beyond mere pattern recognition and into full-fledged logical inference.

By creating a semantic layer over data, knowledge graphs also fuel faster innovation, accelerating experimentation with AI models such as AI Agents. While LLMs act as the ‘brain’ of agents, knowledge graphs help structure context and reasoning, potentially improving agent reliability

And thanks to their integration of structured, semi-structured and unstructured data, knowledge graphs also offer the advantages of scalability and flexibility – for more adaptable data.

Enterprise Knowledge Graphs (EKGs), which are designed specifically for business use cases, have the added benefit of driving enhanced decision-making by uncovering patterns and dependencies in both internal and external data sources.

Four Things to Bear in Mind When Building a Knowledge Graph

If you’re planning to set up a knowledge graph for your organization, be sure to carefully consider the following key points:

• Data modeling: To ensure consistency and scalability, always start with a clearly defined ontology (that is, a structured representation of entities and relationships).

• Data integration: Use knowledge graphs to unify sources that are structured (databases), semi-structured (logs), and unstructured (text and video).

• Governance and technology stack: Maintain data quality by applying metadata and tool standards, version control, and access policies.

• Skill sets: Remember that the success of your knowledge maps depends on having data engineers and ontologists who understand your business domain.

Be Aware of the Challenges

Before embarking on a knowledge-graph-adoption initiative, don’t underestimate the initial complexity. While the tech promises many compelling benefits, you first have to build your graph before you can reap them – and that entails upfront investment in modeling and data integration.

The next challenge is one that will be familiar to regular readers of this blog – data quality. Like many of today’s technologies, knowledge graphs require integration of data from multiple sources. And if data is inconsistent or incomplete, this could significantly impact the accuracy of your graph.

Another all-to-familiar challenge is cultural adoption. Business units generally have their own long-established approaches to storing and sharing data and are often understandably reluctant to move away from them. To lessen the culture shock, it’s important to make sure that you have a dedicated change management program in place ahead of the transition.

Last but no means least, it’s imperative to strike the right balance between security and accessibility. Where control is lacking, there’s a risk of inadvertently exposing sensitive data to unauthorized users. So, be sure to implement identity and access controls that allow your users rapid, reliable access – but only to the data relevant to their roles.

The Road Ahead

AI has made great leaps forward in recent years. But without context, traceability, and reasoning, the tech will struggle to deliver data that meets enterprises’ real-world needs.

That’s where knowledge graphs can play a vital role. These days, data is companies’ most valuable asset – but only if they can use it. Knowledge graphs give organizations the orientation they need to do just that.

What Do You Think?

Want to find out more about knowledge graphs and how to put them to work for your business? Feel free to reach out to me. Or do you have ideas of your own about this tech that you’d like to share? If so, kick off the conversation by leaving a comment below.