Knowledge representation is a term from the fields of Artificial Intelligence, Digital Transformation, and Big Data and Smart Data. It describes how knowledge – that is, information and experience – is stored in such a way that computers can work with it.
For example, if an artificial intelligence is to support medical diagnoses, it must understand how different symptoms and diseases are related. These relationships are captured through knowledge representation, for instance, as lists, networks, or tables, which show how terms and facts fit together. The machine uses this „knowledge“ to find answers or make decisions.
A clear example: Imagine teaching a computer how to classify animals. Knowledge representation gathers characteristics such as „has feathers“ or „can fly“. This allows the system, for instance, to recognise that a sparrow is a bird, as both attributes match.
Knowledge representation is therefore a foundation for machines to not only store information but also to „understand“ and apply it. This subject is becoming increasingly important, especially in the age of artificial intelligence and data-driven processes.
Deutsch 
English (UK)