AI Explained – Knowledge Graphs | Turning Raw Data Into Useful Information

AI Explained – Knowledge Graphs | Turning Raw Data Into Useful Information

Have you ever wondered how social media platforms seem to know you so well? Well, It involves the same mechanism scientists use to predict what new, potential drug molecules might become a groundbreaking treatment–Knowledge Graphs! Watch this video to learn more about how knowledge graphs transform raw data into meaningful connections that allow us to predict outcomes, find new insights, and drive smarter decisions –whether it’s in social media or in predicting how new drugs will interact with the body. Want more AI content? Check out    • AI – Explained   Curious about SandboxAQ? Visit https://www.sandboxaq.com/

Have you ever wondered how social media platforms know exactly what to recommend to you?

Or how scientists predict which new drug might be the next big breakthrough? How are those two things even related?

The secret behind these advancement lies in something called a knowledge graph. But what exactly is a knowledge graph? And why is it so powerful?

Let’s start with the basics.

Imagine you’re watching a soccer game. You can make a pretty simple knowledge graph based on what you see. Represent each player on the field as a node and use a line or edge to connect the nodes based on the actions they perform like passing the ball. This web of players and their actions is an example of a knowledge graph. More generally, nodes represent entities such as people, places, or things, and edges represent the relationships between them. It’s a way of organizing information that highlights connections and interactions. Each node can store rich data. And the soccer example, that could be a player’s statistics, position, or even historical performance. Edge’s can store data too, like waiting that represent the number of passes between players. And once the knowledge graph is complete, you can use it to make predictions about the outcomes of games, to figure out the best players to recruit to a team, and more. So how is a knowledge graph different from a traditional database or spreadsheet? In both cases, information is stored in tables with rows and columns. That’s great for structured data, but it quickly becomes impractical when dealing with complex relationships. A knowledge graph, on the other hand, is like a map of data where you can easily see how everything is connected, and you can perform mathematical operations across different parts of the map. This makes it possible to uncover insights and discover patterns that aren’t immediately obvious in a database or spreadsheet. For example, you can track how frequently players interact, or how various proteins interact in a biological cell.

Knowledge graphs also allow you to easily add new types of relationships among the data, and to also extrapolate from the data, which can be used to infer and validate the applicability of our current knowledge to our predictions. Building and analyzing knowledge graphs involves advanced mathematical and programming tools, and can incorporate machine learning. Software libraries can help manage the graph structures and perform operations, like shortest path analysis, centrality measures, and community detection, while algorithms can calculate similarities and predict new connections or causal relationships.

And knowledge graphs can be used for all sorts of things. In social media, a knowledge graph can map out your connections and interests. If your friend loves a particular restaurant, the platform might recommend that same restaurant to you, based on the strength of the connection to your friend, and based on the other data relating to your food preferences and places you like to visit. In drug discovery, knowledge graphs help scientists manage and analyze vast amounts of biological data.

They can predict how a new drug might interact with proteins in the body, but identifying disease targets for drugs and side effects, potentially saving significant time and resources in the research process. This involves querying the graph to identify potential drug targets and interactions. Knowledge graphs transform the way we store, manage, and analyze data, making it possible to efficiently handle complex data sets, reveal intricate connections, and facilitate powerful data-driven decisions, even when we have limited information about a subject. Whether it’s helping you find your next favorite restaurant or discovering life-saving drugs, knowledge graphs are the web of our shared and validated insights and are shaping the future of technology and science.