Case-Based Reasoning in a Travel Medicine Application - Computational Intelligence in Healthcare: Advanced Methodologies - page 196

et al. [22] introduce the horizontal case representation, a two case base approach
in which one contains the problem and the other one the solutions. They motivate
splitting up the case bases for a more precise case representation, vocabulary and
a simplified knowledge acquisition.
Retrieval strategies have been discussed in the context of Multi-Case-Base
Reasoning in [23]. Leake and Sooriamurthi explain how distributed cases can be
retrieved, ranked and adapted. Although they are dealing with the same type
of case representations of the distributed case bases, both approaches have to
determine whether a solution or part of solution is selected or not. The strategy
of Multi-Case-Base Reasoning is to either dispatch cases if a case-base cannot
provide a suitable solution or to use cases of more than one case base and initiate
an adaptation process in order to create one solution.
Collaborating case bases have been introduced by Ontanon and Plaza [24]
who use a multi-agent system to provide a reliable solution. The multi-agent
system focuses on learning which case base provides the best results, but they
do not combine or adapt solutions of different case bases. Instead their approach
focuses on the automatic detection of the best knowledge source for a certain
question.
Combining parts of cases in order to adapt given solutions to a new problem
has been introduced by Redmond in [25] in which he describes how snippets of
different cases can be retrieved and merged into other cases, but in comparison to
our approach, Redmond uses similar case representations from which he extracts
parts of cases in order to combine them. His approach and the knowledge pro-
vision in SEASALT have in common that both deal with information snippets
and put them together in order to have a valid solution. Further on, Redmond
mostly concentrates on adaptation while we combine information based on a
retrieval and routing strategy.
Our notion of knowledge source properties is comparable to and thus benefits
from advances in the respective field in CBR like the recent work of Briggs
and Smyth [26], who also assign properties, but to individual cases. On the
other hand the graph-like representation of the knowledge sources and its use in
the composition of the final results do not have a direct equivalent in CBR. It
depends on the cases' separation by topic and a clear dependency structure of the
topics (e.g. the country determines the possible diseases, the diseases determine
the respective vaccinations and precautions, etc. ) which is not necessarily given
in traditional CBR.
7 Conclusion and Final Remarks
The SEASALT architecture offers several features, namely knowledge acquisition
from web 2.0 communities, modularized knowledge storage and processing and
agent-based knowledge maintenance. SEASALT's first application within the
docQuery project yielded very satisfactory results, however, in order to further