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

In 2024, the tech world was captivated by vector databases. But while vector databases laid the foundation, 2025 announces an even more revolutionary technology: Knowledge Graphs.

In 2024, the tech world was captivated by vector databases. This technology made it possible to store and analyse unstructured data, such as Word documents and PDFs, in a structured manner. But while vector databases laid the foundation, 2025 announces an even more revolutionary technology: Knowledge Graphs. This new step in AI promises to help organisations use their data smarter, more understandably, and more effectively. 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 efficiently search and analyse complex and unstructured data, such as text and images. As a result, vector databases quickly became indispensable in applications such as search engines and chatbots.

However, 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?

A Knowledge Graph goes beyond simply storing data. It is a way to capture knowledge and context by explicitly defining relationships between concepts. It's like a 'mind map' for AI, where concepts are recorded, along with how those concepts relate to each other.

Suppose you create a Knowledge Graph for contract analysis. Concepts such as 'SLA', 'penalty clause', and 'payment term' are recorded, as well as their interrelationships. For example:

• A 'penalty clause' is dependent on a 'payment term'.
• An 'SLA' contains agreements on 'service levels'.

These relationships help a language model understand how contracts are logically structured. This allows the model to analyse texts and interpret the context and meaning correctly.

Knowledge Graphs in Practice

The power of Knowledge Graphs only truly becomes clear when you look at their applications:

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

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

Imagine an organisation wanting to onboard a new employee quickly. A Knowledge Graph can be used to give the employee 'a quick education' on how specific processes and relationships within the company work.

Why Are Knowledge Graphs So Important?

Generative AI is constantly improving, but it often still lacks an understanding of context and logic within specific domains. Knowledge Graphs offer a solution by 'educating' AI on how the world works within a specific process.

Do you want to teach AI about 'living things'? You can build a Knowledge Graph with:

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

This allows the AI to understand not just the words but also the hierarchy and relationships between them. This makes it possible to achieve much better results within a specific context.

In 2025, Knowledge Graphs will play a major role in the adoption and effectiveness of generative AI and AI agents. They provide the foundation for AI to understand how the world, or a specific process, is structured. This will lead to applications that are smart, and moreover, contextually and logically accurate.

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