Subgroup Mining for Interactive Knowledge Refinement
When knowledge systems are deployed into a real-world application, then the maintenance of the knowledge is a crucial success factor. In the past, some approaches for the automatic refinement of knowledge bases have been proposed. Many only provide limited control during the modification and refinement process, and often assumptions about the correctness of the knowledge base and case base are made. However, such assumptions do not necessarily hold for real-world applications.
In this paper, we present a novel interactive approach for the user-guided refinement of knowledge bases. Subgroup mining methods are used to discover local patterns that describe factors potentially causing incorrect behavior of the knowledge system. We provide a case study of the presented approach with a fielded system in the medical domain.
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