Author: Dr. Stefanie Schwaar
Let's look at a current example from the administration: Every administration gets a special electronic government mailbox (beBPO), i.e., a mailbox where e-mails are received for this administration. Hypothetically, we consider the case of a city where communication is completely digitized and takes place via a mailbox, i.e., via a specific e-mail address. The incoming e-mails should now be assigned to the appropriate offices directly when they arrive, such as registering a marriage at the registry office or reporting a suspected case of Corona at the health department. This assignment must be learned, which means: our data are the e-mails and the classes are our offices. How can an algorithm learn this association?
Data: Information and Knowledge
We start with our data and assume that the information we are interested in comes from that data – a basic assumption not to be forgotten. Specifically, this means that all the data needed to make a good enough prediction or detection or processing is available. However, the context is unknown or too complex to describe with a simple model. Graphically, we imagine it like this:
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