From Text To Cases: Machine Aided Text Categorization for Capturing Business Reengineering Cases

Catherine BaudinPrice Waterhouse TechnologyCentrebaudin @ tc.pw. comScott WatermanPrice Waterhouse TechnologyCentrewaterman @ tc.pw. comAbstractSharing business experience, such as client engagements, proposals or best practices, is an important part of theknowledge management task within large business organizations. While full text search is a first step at accessingtextual material describing corporate experience, it does not highlight important concepts and similarities betweenbusiness practices structured or operated differently. Conceptual indexing languages, on the other hand, are highlevel indexing schemes based on taxonomies of domain concepts designed to provide a common language todescribe, retrieve, and compare cases. However, the effective use of these high level languages is limited by the factthat they require users to be able to *describe cases in terms an often large body of controlled vocabulary. The mainchallenge to using CBR and data mining technology for accessing and analyzing corporate knowledge is not indesigning sophisticated inference mechanisms, but is in representing large bodies of qualitative information intextual form for reuse. This knowledge representation task is the process of mapping textual information to pre-defined domain models designed by knowledgeable domain experts. We are experimenting with machine aided textcategorization technology to support the creation of quality controlled repositories of corporate experience in thebusiness domain.1. IntroductionSharing business experience, such as client engagements, proposals or best practices is an "important part of the knowledge management task within large business organizations. In thebusiness domain, cases are usually textual documents about business processes, whatmethodology or technology was key to the success (or failure) of the operation, or what criteriawere used to measure the results. While full text search is a first step at accessing textual.material describing corporate experience, it does not highlight important concepts andsimilarities between st6ries describing business practices structured or operated differently:The key to effectively capturing and reusing best practices and corporate experience is to be ableto describe textual documents in terms of a structured language representing key concepts of thedomain. Fully automated text processing techniques such as unsupervised text clustering are notlikely to detect key domain concepts in complex textual documents that refer to multipl e topics.When processing qualitative information such as business cases, the stories must be mapped topre-defined domain models designed by knowledgeable domain experts. Conceptual indexinglanguages in the business domain are high level indexing schemes that highlight importantaspects of the cases and their similarities. For example, indexing languages such as theInternational Business Language TM (Emerson, 1996) or the Process Classification Framework(APQC, 1992), provide a common language to describe and compare business cases.Unlike applications where cases are described by sets of numerical attributes (price of a PC, sizeof the hard disk in sales support applications~) or by a small set of possible symbolic labels(processor type: pentium_166), mapping text documents to a large controlled vocabulary is not51