From Vector Databases to Knowledge Graphs: What Does This Mean for the Future of AI?

In 2024, the tech world was under the spell of vector databases. This technology made it possible to store and analyse unstructured data, such as Word documents and PDFs, in a structured way. But while vector databases laid the foundation, 2025 announces an even more revolutionary technology: Knowledge Graphs. This new step in AI promises to help organizations use their data in a smarter, more understandable and more effective way. But what are Knowledge Graphs, and why are they so important?

What are Vector Databases?

To understand the importance of Knowledge Graphs, we first need to briefly consider vector databases. Vector databases are databases that translate data into numerical representations, or vectors. This makes it possible to search and analyse complex and unstructured data, such as text and images, in an efficient way. As a result, vector databases quickly became indispensable in applications such as search engines and chatbots.

But while vector databases are good at retrieving data, they tell us nothing about the meaning and relationships within that data. This is where the power of Knowledge Graphs comes in.

What are Knowledge Graphs?

One Knowledge Graph goes beyond storing data. It is a way of capturing knowledge and context by explicitly defining relationships between concepts. It's like a “mind map” for AI, not only recording concepts, but also how those concepts are interrelated.

How does that work?

Let's say you're creating a Knowledge Graph for contract analysis. Terms such as “SLA”, “penalty clause” and “payment term” are defined, as well as their relationships with each other. For example:

• A “penalty clause” depends on a “payment term”.
• An “SLA” contains agreements about “service levels”.

These relationships help a language model understand how contracts work logically. As a result, the model can not only analyze texts, but also the context and meaning interpret correctly.

Knowledge Graphs in Practice

The power of Knowledge Graphs only really becomes clear when you look at the applications:

1. Contract analysis

With a Knowledge Graph, you can teach AI what terms and relationships are important in contracts. Think of penalty clauses, payment terms and SLAs. This makes it possible to identify risks and opportunities automatically.

2. Complex business processes

Companies can use Knowledge Graphs to optimize processes such as supply chain management or customer service. By establishing explicit relationships, AI can work more effectively and accurately.

3. Knowledge transfer

Imagine that an organization wants to train a new employee quickly. A Knowledge Graph can be used to provide employees with “rapid training” about how specific processes and relationships within the company work.

Why Are Knowledge Graphs Crucial?

Generative AI is getting better, but there is often still a lack of understanding the context and logic within specific domains. Knowledge Graphs offer a solution here by providing AI with “training” about how the world works within a given process.

A simple example:

Want to teach AI about “living things”? Then you can build a Knowledge Graph with:

• Living things > Plants, animals, people.
• Animals > Wild animals, pets.
• Wild animals > Lions, tigers.

As a result, the AI not only understands the words, but also the hierarchy and relationships in between. This makes it possible to achieve much better results within a specific context.

In 2025, Knowledge Graphs will play a critical role in the adoption and effectiveness of generative AI and AI agents. They provide the basis for helping AI understand how the world — or a specific process — works. This will lead to applications that are not only smart, but also contextually and logically accurate.