Knowledge graph embeddings belong to the world of Artificial Intelligence and are becoming increasingly important in the fields of Big Data and Smart Data. They help to link large amounts of information in such a way that computers can understand and use it better.<\/p>\n
Imagine a knowledge graph as a vast network where knowledge is interconnected as nodes \u2013 for example, people, places, and things. Knowledge graph embeddings convert these nodes and their relationships into mathematical values, enabling algorithms to work with them efficiently. This makes it possible to identify hidden connections in data and deliver better recommendations or search results.<\/p>\n
A simple example: An online shop wants to suggest products to customers in a targeted way. With knowledge graph embeddings, the system can recognise that a customer who likes to buy \u201eoutdoor jackets\u201c might also be interested in \u201ehiking boots\u201c, because both terms are closely connected in the knowledge network. This makes suggestions more personal and relevant.<\/p>\n
For businesses, the use of knowledge graph embeddings means they can gain even more useful insights from their data and make their processes smarter.<\/p>","protected":false},"excerpt":{"rendered":"