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

Catherine Baudin and Scott Waterman

Sharing business experience, such as client engagements, proposals or best practices, is an important part of the knowledge management task within large business organizations. While full text search is a first step at accessing textual material describing corporate experience, it does not highlight important concepts and similarities between business practices structured or operated differently. Conceptual indexing languages, on the other hand, are high level indexing schemes based on taxonomies of domain concepts designed to provide a common language to describe, retrieve, and compare cases. However, the effective use of these high level languages is limited by the fact that they require users to be able to *describe cases in terms an often large body of controlled vocabulary. The main challenge to using CBR and data mining technology for accessing and analyzing corporate knowledge is not in designing sophisticated inference mechanisms, but is in representing large bodies of qualitative information in textual form for reuse. This knowledge representation task is the process of mapping textual information to predefined domain models designed by knowledgeable domain experts. We are experimenting with machine aided text categorization technology to support the creation of quality controlled repositories of corporate experience in the business domain.


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