Underlying assumptions of knowledge acquisition and machine learning

Abstract

The paradigm of knowledge-based systems has become of practical interest to a broad variety of persons: software engineers, knowledge engineers and domain experts. Therefore, it becomes necessary to make explicit the underlying assumptions of the field. In this paper, the terms “knowledge” and “modeling” as they occur in texts on knowledge acquisition and machine learning are investigated. It is shown that the terms are used with very different meanings corresponding to different views of knowledge acquisition. The transfer view, the performance or building-blocks view, the knowledge-level or stepwise refinement view, and the constructive view of knowledge and its acquisition are described. The implications for designing systems which support a user in constructing a knowledge base are indicated. In particular, it is stressed that systems must support revisions of all modeling decisions if we want to prevent from the next bottleneck, the bottleneck of knowledge-base maintenance.