Abstract
It has been widely advertised that the numerous large databases which exist in the various industries, administrative offices and in the public domain (e.g. the world-wide-web) would indeed be very valuable data mines from which important and previously unknown knowledge could be harvested when machine learning procedures would be applied as mining tools. The present research evaluates the prospects of discovering such knowledge from industrial databases. Three different databases are considered and three different machine learning tools (conceptual clustering, neural net, and inductive logic programming) are applied in an experimental fashion. From these experiences it could be concluded that the tool box philosophy has severe limitations in highly structured industrial application areas. It was thus suggested that higher order conceptualizations of machine learning should be developed which are easier to apply and understand by the user. A preview of the KOALA system which is currently under development is then presented. By applying constraint satisfaction over hierarchically structured domains, the KOALA system allows the user to have his own machine learning application being configured according to the domain ontologies and the specific needs of a given field of application.
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Schmalhofer, F., Kozieja, C. (1996). Some late-breaking news from the data mines and a preview of the KOALA system: A prospector's report. In: Shadbolt, N., O'Hara, K., Schreiber, G. (eds) Advances in Knowledge Acquisition. EKAW 1996. Lecture Notes in Computer Science, vol 1076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61273-4_14
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DOI: https://doi.org/10.1007/3-540-61273-4_14
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