Knowledge graph analytics is primarily at home in the fields of Big Data and Smart Data, Artificial Intelligence, and Digital Transformation. The term describes methods and tools used to filter and analyse information from so-called knowledge graphs.<\/p>\n
A knowledge graph represents information as a large, interconnected network. Nodes represent individual terms or objects, and edges show their relationships to each other. Knowledge graph analytics helps companies analyse these many connections, identify patterns, and make visible the interdependencies that remain hidden in traditional databases.<\/p>\n
A clear example: An online shop uses knowledge graph analytics to understand how products, customer interests, and reviews are related. This allows it to give better product recommendations because the analytics also recognise indirect relationships \u2013 for instance, between similar products or customer groups.<\/p>\n
Knowledge graph analytics offers a significant advantage, particularly when processing large volumes of data. Companies gain deeper insights and can make more informed decisions. Therefore, knowledge graph analytics is a modern tool for extracting new knowledge from large, complex datasets for their own business.<\/p>","protected":false},"excerpt":{"rendered":"