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Fig. 2. Knowledge model representing the geographical location of countries
from taxonomies, because countries that share a node also might have features
in common that can be applied completing incomplete case as described in Bach
et. al. [6]. Along with taxonomies, similarity measures for symbolic representa-
tions can also be realized using tables, ontologies or individually defined data
strucutres.
After the adaptation process has been executed the new case has to be revised.
The revision can either be realized using again background knowledge or external
feedback. In our example we send our solution to an expert who revises the
case manually and gives feedback. Afterwards we have a new revised problem-
solution pair (case) that can be included in the case base. In this way the case
base and thus the whole CBR system is able to learn and to adapt to different
circumstances.
4 Underlying Architecture
docQuery will be an intelligent information system based on experts which are
distributed all over the world and use the platform giving information to travelers
and colleagues. The implementation will pursue an approach mainly based on
software agents and CBR. Both software agent and CBR have already been
used to implement experience based systems [7,4,8]. docQuery will use different
knowledge sources (diseases, medications, outbreaks, guidelines, etc.) which are
created in cooperation with experts, provided in databases and maintained by
the users of docQuery. However, medicine cannot deal with vague information
how they might occur in extractions of community knowledge. Therefore we also
integrated data bases as knowledge sources in case exact matches are required.
Collaborative Multi-Expert-Systems (CoMES) are a new approach presented
of Althoff et. al.[9] which presents a continuation of combining established
 
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