Case-Based Reasoning in a Travel Medicine Application - Computational Intelligence in Healthcare: Advanced Methodologies - page 189

The Apprentice Agent is meant to support the Knowledge Engineer in for-
malizing relevant posts for insertion in the Topic Agents' knowledge bases. It
is trained by the Knowledge Engineer with community posts and their formal-
izations. The apprentice agent is currently being developed using GATE [13]
and RapidMiner [16]. We use a combined classification/extraction approach that
first classifies the contributions with regard to the knowledge available within
the individual contributions using term-doc-matrix representations of the con-
tributions and RapidMiner then attempts to extract the included entities and
their exact relations using GATE. Considering docQuery's sensitive medical ap-
plication domain we only use the Apprentice Agent for preprocessing. All its
formalizations will have to be reviewed by the Knowledge Engineer, but we still
expect a significantly reduced workload for the Knowledge engineer(s).
Although CoMES is a very new approach, the used techniques, like the Ex-
perience Factory[7], Case-Based Reasoning or Software Agents are well known.
docQuery will integrate those techniques in a web community and creating an
intelligent information system which is based on the knowledge of experts, experi-
ences discussed on discussion boards and novelties presented by travel medicines
that are a part of the community. Sharing knowledge at this level furthers the
web 2.0 approach and allows us to develop new techniques.
5 Combination of Heterogeneous Knowledge Sources
When dealing with complex application domains it is easier to maintain a number
of heterogeneous knowledge sources than one monolithic knowledge source. The
knowledge modularization within SEASALT is organized in the Knowledge Line
that is based on the principle of product lines as it is known from software
engineering [17] and we apply it to the knowledge in knowledge-based systems,
thus splitting rather complex knowledge in smaller, reusable units (knowledge
sources). Moreover, the knowledge sources contain different kinds of information
as well as there can also be multiple knowledge sources for the same purpose.
Therefore each source has to be described in order to be integrated in a retrieval
process which uses a various number of knowledge sources (see the third layer
(Knowledge Line) in Figure 3).
The approach presented in this work does not aim at distributing knowledge
for performance reasons, instead we are planning to specifically extract infor-
mation for the respective knowledge sources from WWW communities or to
have experts maintaining one knowledge base. Hence, we are creating knowledge
sources, especially CBR systems, that are accessed dynamically according to the
utility and accessibility to answer a given question. Each retrieval result of a
query is a part of the combined information as it is described in the CoMES
approach [18].
For each specific issue a case or data base will be created to ensure a high
quality of knowledge. The data structure of each issue is different and so is the
case format and domain model. Creating high quality “local knowledge bases”
will guarantee the high quality of the systems knowledge.