In order to handle the ever increasing amount of digital data efficiently, methods and techniques for automation have to be available. A key issue is the use of knowledge-based systems to interpret the data and operate on it in an automatic way. Interpretation requires knowledge about the underlying concepts in the data. Providing the necessary knowledge, however, is a bottleneck in automation. In this paper, an approach to semi-automatic knowledge acquisition is described. The knowledge is derived from given example data using supervised machine learning. An application of the approach in the domain of the interpretation of unstructured spatial data is given, and possible further applications are outlined.