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maintainer in case old or erroneous data has to be removed or corrected. To
follow this goal the maintenance processes has to be created along with the data
models regarding the interfaces and the applications built upon them.
To ensure up-to-date data the system has to be checked by experts regularly,
and by integrating a web community new topics will have to be identified and
new cases will have to be entered in the knowledge sources. For that purpose
processes for updating (inserting, maintaining, deleting, extending, etc.) have
to be implemented and established. For instance, we assume that a group of
experts takes care of new entries in docQuery: In this case we are assigning
topics with the expert's field of expertise to each of them and if there is a new
discussion in the respective area detected, this is e-mailed to the expert so he
oder she can follow this discussion. Further on, when the system extracted and
processed information the complete set which should be inserted is sent to the
expert and has to be approved before it can be inserted in the according case
base. This proceeding is not for any application domain necessary, but since we
deal with medical information we have to make sure that correct information
are provided, although we are only giving information that do not substitute a
medical consultation.
Even if we have different kinds of Topic Agents and their according Case Fac-
tories, the behavior of some Case Factory agents (like the new case inserter) can
be reused in other Case Factories of the same Knowledge Line. We differentiate
between agents that handle general aspects and are contained in any Case Fac-
tory and agents that are topic-specific and have to be implemented individually.
General Case Factory agents usually focus on the performance or regular tasks
like insertion, deletion, merging of cases. Topic specific Case Factory agents are
for example agents that transfer knowledge between the knowledge containers [5]
or define certain constraints and usually they have to be implemented for an in-
dividual topic considering its specifications or fulfilling domain dependent tasks.
The Knowledge Line retrieves its information, which is formalized by a Knowl-
edge Engineer and/or machine learning algorithms, from knowledge sources like
databases, web services, RSS-feeds, or other kinds of community services and
provides the information as a web service, in an information portal, or as a part
of a business work flow. The flexible structure of the knowledge line allows de-
signing applications incrementally by starting out with one or two Topic Agents
and enlarging the knowledge line, for example with more detailed or additional
topics, as soon as they are available or accessible.
6 Related Work
The approach of distributed sources has been a research topic in Information
Retrieval since the mid-nineties. An example is the Carrot II project [21], which
also uses a multi-agent-system to co-ordinate the document sources. However,
most of our knowledge sources are CBR-systems, which is the reason why we
concentrate on CBR-approaches. The issue of differentiating case bases in order
to be more suitable for its application domain has been discussed before. Weber
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