Maintaining Unstructured Case Bases

Kirsti Racine1 and Qiang Yang1

School of Computing Science
Simon Fraser University
Burnaby BC V5A 1S6,
Canada
Email : kracine@cs.sfu.ca, qyaug@cs.sfu.ca
Web : http://fas.sfu.ca/kracine



Abstract. With the dramatic proliferation of case based reasoning systems 
in commercial applications, many case bases are now becoming
legacy systems. They represent a significant portion of an organizations
assets, but they are large and difficult to maintain. One of the contributing 
factors is that these case bases are often large and yet unstructured;
they are represented in natural language text. Adding to the complexity
is the fact that the case bases are often authored and updated by different 
people from a variety of knowledge sources, making it highly likely
for a case base to contain redundant and inconsistent knowledge.
In this paper, we present methods and a system for maintaining large
and unstructured case bases. We focus on two difficult problems in case-base 
maintenance: redundancy and inconsistency detection. These two
problems are particularly pervasive when one deals with an unstructured
case base. We will discuss both algorithms and a system for solving these
problems. As the ability to contain the knowledge acquisition problem
is of paramount importance, our methods allow one to express relevant
domain expertise for detecting both redundancy and inconsistency naturally 
and effortlessly. Empirical evaluations of the system prove the
effectiveness of the methods in several large domains.
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