💡 What is a knowledge graph?

Knowledge graphs: a friendly definition

You may have heard the term “knowledge graph” used recently, especially in relation to artificial intelligence or large technology companies like Google, Microsoft, and Facebook using them for all sorts of reasons. But what exactly is a knowledge graph and why are they useful?

 

According to Google Trneds, the interest has been increasing steadily since an initial spike over a decade ago.

Imagine a giant spider web. At each connection point, there’s a bit of information: maybe a fact about a person, a place, a thing, or even an intangible concept. These bits of information are connected by threads in the web that represent the relationships between them. This is what a knowledge graph is like. It’s a vast, interconnected network of facts and how those facts relate to each other.

For example, if you do a Google search for “Brad Pitt”, a knowledge graph may pull information showing that Brad Pitt is an actor who starred in movies like Fight Club and Once Upon a Time in Hollywood. It would also link him to related entities like Angelina Jolie, his former wife. This allows Google to display useful snippets of information prominently in the search results so you get answers faster.

When someone (or an integrated computer system) in a company looks for information, a knowledge graph helps them see not only the piece of data they’re looking for but also how it connects to other relevant information. It’s like having a map that shows you not just where you are but also the interesting places nearby and the paths you can take to get there.

Knowledge graph use cases and benefits

  1. Finding connections: They help uncover relationships between pieces of information that might not be obvious at first glance. For example, linking customer feedback to specific product features or identifying common issues in customer service inquiries.
  2. Automatic tagging: Because they understand the core concepts relevant to your
  3. Recommendation: Recommendations are a multi-faceted power. You can recommend anything from related content to recommending “Next best action (NBA)” or “next best engagement (NBE)” – which means automatically surfacing insights or content to customers or staff to take the action they see fit. All this can be driven via intranets, extranets, apps, or even the metaverse based on the best available contextual data.
  4. Enhancing search: When you search for something in a company’s system, a knowledge graph can help by understanding the context of your search and bringing back more relevant results.
  5. Improving decision-making: By providing a comprehensive view of the company’s data and how it all relates, knowledge graphs can support better, more informed decision-making.
  6. Personalization: For businesses that interact directly with customers, knowledge graphs can help tailor experiences, recommendations, and services to individual needs and preferences based on the interconnected data.
  7. Innovation: By making it easier to see how different pieces of information relate to each other, knowledge graphs can spark new ideas and ways of thinking about problems or opportunities.

In simple terms, a knowledge graph is like a super-smart librarian who knows not only where every book is but also how each book relates to every other book in the library. It’s a powerful tool for businesses to organize, search, and leverage their data in ways that were previously impossible or very difficult to achieve.

Inside large companies, knowledge graphs power many intelligent services by understanding facts and relationships at scale. From personalized content recommendations (e.g. Facebook uses one) to smart searching and question answering (e.g. Google and Amazon Alexa use them), knowledge graphs are the brains behind many modern AI applications. They allow these services to connect dots between people, places, things and ideas so they can better serve their users.

How do knowledge graphs work in an enterprise tech stack?

In a business setting, this spider web can stretch across the entire company (or just across closely related systems), connecting all sorts of data. Ideally, it could be data from different departments—sales, customer service, marketing, product development, and more. In practice, it’s often related departments who have a specific business need to connect what they do for internal or external users, or even AIs.

Knowledge Graph are gaining traction in the enterprise technology stack because they address key gaps in traditional table-based relational systems:

Flexible Connectivity: KG’s bring a new level of data and content connectivity, transforming how businesses can organise and deliver content across multiple channels. Tech Republic tells us Ofer Bengal, CEO at Redis Labs, cites three big trends drive database market growth: open source, non-relational data (like KGs), and cloud.

New Applications: From enhancing search functionalities to powering recommendation systems and auto-taggers, knowledge graphs are at the heart of creating intuitive, human-centric, scalable digital experiences.

Integrated Digital Experiences: As we move towards more integrated digital landscapes, the role of knowledge graphs in creating meaningful and context-aware links across various systems is becoming increasingly critical.

How do knowledge graphs support AI?

A study by data.world saw that knowledge graphs can vastly improve AI accuracy and performance.

Study showing the superiority of knowledge graphs vs regular databases

Leveraging knowledge graphs on the web and beyond

In summary, knowledge graphs are structured webs of facts that help computers broadly understand the world.

By encoding millions of interrelated facts, they enable intelligent applications to surface relevant information and insights faster.

So next time you get a useful recommendation or a quick answer from a digital system – be that a website, app, or even an in-game link, maybe inside virtual reality – there may well be a knowledge graph behind the scenes.

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