Redundancy and Inconsistency Detection in Large and Semi-Structured Case Bases
IEEE Transactions on Knowledge and Data Engineering, (1998)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
organization's 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 semi-structured 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 a semi-structured 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.