AI models have become remarkably good at generating answers, perhaps better than we ever thought possible. But, behind the scenes, there’s a less flashy technology helping many AI systems understand how information connects, and these are called knowledge graphs.
For AI companies, knowledge graphs can provide a structured way of representing information, showing not just individual facts but the relationships between them. And as AI becomes increasingly reliant on vast amounts of information, that ability to connect the dots is becoming increasingly useful. Because without context and understanding, information means very little.
What Is A Knowledge Graph?
Basically, a knowledge graph is a way of organising information around entities and the relationships between them. Rather than just storing information as isolated pieces of data, a knowledge graph connects things together. A person might be linked to a company, which is linked to an industry, which is linked to a particular location or product. It’s kind of like a huge web of connected information.
For example, a knowledge graph could understand that OpenAI is a company, that Sam Altman is associated with OpenAI, that OpenAI develops AI models and that those models can be used for applications such as software development. From there, these relationships can then be queried and analysed by software.
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Why Do AI Companies Care About Them?
Large language models are trained on enormous amounts of information, but that doesn’t necessarily mean they have a perfect understanding of how every piece of information relates to another. Knowledge graphs can provide a more structured layer of information for AI systems to work with.
This can be especially useful when an AI application needs to retrieve specific information, understand relationships or reason across multiple connected pieces of data.
For example, an AI tool designed for financial research could use a knowledge graph to connect companies with their executives, subsidiaries, investors, financial results and industries. So rather than just searching for individual pieces of information, the system can follow those relationships. That can make AI applications more useful when dealing with complicated or highly specialised information.
Knowledge Graphs And AI Hallucinations
One of the biggest challenges with generative AI is the risk of hallucination. That is, when a model produces information that sounds convincing but is incorrect.
Of course, knowledge graphs don’t magically eliminate hallucinations, but they certainly can give AI systems a more structured source of information to draw from.
This is one reason they’re particularly interesting for businesses building AI products where accuracy matters. A startup building an AI healthcare, legal or financial application, for example, might need its system to work with specific, carefully organised information rather than relying entirely on a general-purpose language model.
How Are Startups Using Knowledge Graphs?
Now, the technology isn’t only relevant to major AI companies. Startups can use knowledge graphs to build specialised AI applications around their own data. A company might create a graph connecting its customers, products, documents, transactions and internal knowledge, then allow an AI system to query those relationships.
This could be useful for pretty much everything from enterprise search and recommendation engines to fraud detection, customer support and research tools. It also creates an interesting opportunity for startups: rather than competing to build another general-purpose AI model, they can build specialised products around unique data and the relationships within it.
Does That Mean That Knowledge Graphs Will Replace AI Models?
AI models aren’t going to be replaced by knowledge graphs, because in many ways, the two technologies are complementary. A language model is particularly good at understanding and generating natural language, while a knowledge graph can provide structured information and explicit relationships.
So if you put the two together, an AI application can potentially combine conversational reasoning with a more organised source of knowledge. As AI moves beyond simply generating text towards systems that can search, reason, use tools and take action, understanding this will become more important than ever.
