Exploiting inductive bias shift in knowledge acquisition from ill-structured domains
Machine Learning (ML) methods are very powerful tools to automate the knowledge acquisition (KA) task. Particularly, in illstructured domains where there is no clear idea about which concepts exist, inductive unsupervised learning systems appear to be a promising approach to help experts in the early stages of the acquisition process. In this paper we examine the concept of inductive bias, which have received great attention from the ML community, and discuss the importance of bias shift when using ML algorithms to help experts in constructing a knowledge base (KB) A simple framework for the interaction of the expert with the inductive system exploiting bias shift is shown. Also, it is suggested that under some assumptions, bias selection in unsupervised learning may be performed via parameter setting, thus allowing the user to shift the system bias externally.
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