English Abstract Knowledge engineering promises to extend the range of
tasks that can be automated and to make information systems more accessible
to end-users such as policy makers. It can make knowledge more widely
available and improve its consistency. Neural networks could be used for
pattern recognition and data analysis tasks, machine learning techniques can
alleviate the acquisition and maintenance of knowledge bases. This spring
the Informatics Service Center has started a project to explore these
techniques and promote their use in promising areas. At present the use of
these techniques at the RIVM is still limited. This report describes
projects in which use of these techniques was attempted or contemplated.
Apart from a number of exploratory studies, knowledge engineering is used in
two systems, a toxicological information system for physicians and a support
system for the selection of measures to obtain reduced emission levels.
Neural networks are used for the interpretation of infrared spectra of
ground samples. Given the mission of the RIVM as a knowledge broker and
given the growing integration of knowledge engineering and main-stream
information technology and the promise of new technologies such as automated
induction and neural networks, there is every reason actively to pursue the
use of these techniques at the RIVM. Potential application areas will be
further elaborated in future reports.