Motivation

Machine learning helps to solve complex tasks based on real-world data instead of pure intuition. It is most suitable for building AI systems when knowledge is not known, or knowledge is tacit. Knowledge engineering, on the other hand, is appropriate for representing expert knowledge, which people are aware of and that has to be considered for compliance reasons or explanations.

Many business cases and real-life scenarios using machine learning methods, however, demand explanations of results and behavior. This is particularly the case where decisions can have serious consequences. Furthermore, application areas such as banking, insurance and medicine, are highly regulated and require compliance with law and regulations. This specific application knowledge cannot be learned but needs to be represented, which is the area of knowledge engineering.

Knowledge-based systems that make knowledge explicit often based on logic and thus can explain their conclusions. These systems typically require a higher initial effort during development than systems that use unsupervised learning approaches. However, symbolic machine learning and ontology learning approaches are promising for reducing the effort of knowledge engineering.