Symbolic machine learning is a term used in the fields of artificial intelligence, digital transformation, big data and smart data. It involves machines learning in the same way that humans process knowledge using symbols and rules. This means that the system does not simply analyse data, but also understands how different concepts are related and can derive rules from this.
In contrast to other machine learning approaches, in which computers recognise patterns from huge amounts of data, symbolic machine learning works with defined symbols and logical rules. This makes it easier to understand and explain the machine's behaviour, which is particularly important when decisions need to be transparent.
An example: Imagine a programme is supposed to process customer service enquiries. Symbolic machine learning helps to correctly assign the meaning of words such as "invoice", "complaint" and "delivery" and to generate meaningful responses. The machine is therefore given rules such as "If the word 'complaint' occurs, then forward the enquiry to the complaints department." This makes the application safe, comprehensible and particularly attractive for companies.
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