Knowledge Graphs Explained: Their Past, Present, and Future in AI Search

This post explains the history of knowledge graphs — and how they help brands deliver accurate, structured answers wherever customers search.

If you've ever asked your phone a question like, "Who invented the telephone?" or "Where's the nearest pizza place open now?" and gotten an answer right away, that's thanks to a knowledge graph. It's like a giant digital brain that helps computers understand information the way people do.

Today, knowledge graphs are often discussed as part of the AI revolution. That makes sense: they're used to train large language models (LLMs), power chatbots, and make machines smarter. But knowledge graphs have been quietly shaping the way we find information for years — since long before AI became a buzzword.

Here's how knowledge graphs started — and how brands can use them today to deliver the right answers to their customers, every time.

A long time ago (well, the 1960s), computers were just starting to store and process information. But there was a problem: they didn't really "understand" that information. Instead, they could only match exact words or numbers – meaning they weren't great at recognizing relationships between concepts. (Imagine a library where every book is dumped in a pile, but you have to find one specific sentence. Frustrating, right?)

Fast-forward to the 1970s and 1980s, and researchers began innovating to make computers smarter. Early attempts at semantic networks — the ancestors of knowledge graphs — allowed machines to make connections between ideas. (For example, linking the name "Alexander Graham Bell" to the invention of the telephone.) These were like the first building blocks of a more connected, intelligent way to process information.

Then, the internet was born in the 1990s. Suddenly, there was more information than ever — but finding what you needed wasn't easy. Search engines, like Yahoo and AltaVista, emerged to help people find what they did.

But these early search engines had a major flaw: they relied on simple keyword matching. If you searched for "apple," you might get results for fruit, Apple computers, or a random recipe blog – because the search engine couldn't understand context.

This sparked a breakthrough question: what if search engines could understand the relationships between words, the way a human brain can? What if they could recognize that "inventor of Apple" refers to a person (Steve Jobs) who founded a company, and not the fruit. This idea became the foundation of modern knowledge graphs.

In 2012, Google officially introduced its Knowledge Graph, a database containing billions of facts about people, places, and things. It was structured in a way that finally allowed machines to understand those "relationships between words". This was a game-changer.

Now, instead of just showing a list of links in response to a search query, Google could provide quick, clear answers. If you searched for "Who is Beyoncé?" you'd see her picture, a short bio, her albums, and even related people like her husband, Jay Z — all thanks to a knowledge graph organizing that information behind the scenes. Basically, it created the "brainlike" data framework engineers had been talking about for a long time.

Seeing its power, other companies quickly began building their own knowledge graphs to power smarter websites, apps, and devices. The idea was simple but powerful: connect dots between facts so computers could answer questions in a way that feels natural.

It's all led up to today. AI-driven search experiences (like Gemini, ChatGPT, Meta AI, and other AI models) aren't just crawling indexed web pages.

Instead, AI search pulls from a wide range of sources and delivers quick, informed, and conversational answers to questions (and customers trust what AI search shares).

For brands, this means one thing: If your data isn't structured and accessible in a knowledge graph, AI search might not find you. And given that AI-driven search experiences are growing fast, that's a problem.

A knowledge graph makes sure your brand's information — like store hours, services, product details, and FAQs — is structured in a way that AI-powered platforms can understand. That means your brand shows up when and where customers need answers.

Allow us to talk about, well, us for a minute. Because while the term "knowledge graph" is gaining popularity, we've been experts in this space for years.

Unlike generic knowledge graphs, which might help answer basic trivia questions, Yext's Knowledge Graph is built for brands. It's designed to help brands structure their information in a way that makes it discoverable across search engines, AI platforms, and really, every digital touchpoint.

Think about it: A customer asks ChatGPT, "Is XYZ Hotel pet-friendly?" or "What's the phone number for my bank?" If that brand is using the Yext Knowledge Graph, their information is structured and ready to surface in AI-driven answers — accurately and consistently.

Plus, Yext's Knowledge Graph adapts as brands grow and change. It's not just a static database; it's a dynamic system (again, like a brain!) that makes sure your brand's information stays up to date everywhere customers search. It's like having a super-smart assistant that keeps track of everything for you, so you can focus on what matters most: making your customers happy.

Knowledge graphs might sound complicated, but they've been quietly powering search experiences for over a decade. They weren't created in response to AI; they're a foundational part of what has made search algorithms and LLMs so capable of answering natural language questions over the past decade.

And now, as AI transforms the way people find information, knowledge graphs have never been more important.

From helping customers find the nearest coffee shop that's open now to making sure product availability is always accurate, knowledge graphs make sure brands can deliver the right answers in the right places, fast.

Want to learn more about structuring your data to surface in AI search? Click here.