Broadening the Applicability of Relational Learning

Inductive Logic Programming (ILP) provides an effective method of learning logical theories given a set of positive examples, a set of negative examples, a corpus of background knowledge and specification of a search space from which to compose the theories. While specifying positive and negative examples is relatively straightforward, composing effective background knowledge and search space definition requires detailed understanding of many aspects of the ILP process and limits the usability of ILP. This research explores a number of techniques to automate the use of ILP for a experts whose expertise lies outside of ILP. These techniques include automatic generation of background knowledge from user-supplied information in the form advice about specific training examples, utilization of type hierarchies to constrain search, and an iterative-deepening style search process. Additionally, I examine methods of knowledge acquisition through human-computer interfaces, facilitating the use of ILP by the novice user.

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