Details

Autor(en) / Beteiligte

• Thieme, Sabine
• Morik, Katharina

Titel

The acquisition of model-knowledge for a model-driven machine learning approach

Ist Teil von

• Knowledge Representation and Organization in Machine Learning, 2005, p.177-191

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2005

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Knowledge acquisition systems with a model-driven learning mechanism require the representation of that model in the system. The model which guides the learning mechanism must be distinguished from the knowledge (domain model) which is to be learned with the learning mechanism; only the former is the concern of this paper. If the model for guiding the learning mechanism is to be enlarged and improved while working with such a system, the acquisition and representation of new parts of this model must be supported. In addition to the insertion of new parts into the existing model, it is very important to consider redundancy, integrity and completion, because the quality of the model influences the quality of the learning capabilities of the knowledge acquisition system. In this paper, we present the acquisition facilities for meta-knowledge in the knowledge acquisition system BLIP. The meta-knowledge represents the model used by the learning mechanism in BLIP. It mainly consists of ruleschemes, which describe sets of possible rules in different domains concerning the structure of these rules. The chief task is to acquire new ruleschemes.