Knowledge graph embedding

A knowledge graph is like a big library of information, like a giant encyclopedia, but on the computer. It helps us understand how different things are connected to each other. For example, it might show how cats are related to dogs, or how countries are related to their capitals.

But sometimes, this information can be too much for us to understand. That's where knowledge graph embeddings come in to help!

Think of knowledge graph embeddings like stickers or magnets that we can put on the different pieces of information in the knowledge graph to make it easier to understand. These stickers tell us how important each piece of information is, and how it's connected to other pieces of information.

To do this, a computer algorithm looks at the knowledge graph and tries to find patterns in the information. Then it assigns a special code, or vector, to each piece of information in the graph. These vectors are like secret codes that only the computer can understand, but they help the computer know how each piece of information relates to others in the graph.

Once the algorithm has created these vectors, we can use them to search the knowledge graph more easily. For example, we can ask the computer to find all the countries that have a specific type of government, or all the animals that like to live in the same kind of habitat.

Overall, knowledge graph embeddings help us make sense of the huge amount of information in a knowledge graph, so we can understand how everything is connected and find the answers we're looking for more quickly and easily!