Learning link-based classifiers from ontology-extended textual data

Real-world data mining applications call for effective strategies for learning predictive models from richly structured relational data. In this paper, we address the problem of learning classifiers from structured relational data that are annotated with relevant meta data. Specifically, we show how to learn classifiers at different levels of abstraction in a relational setting, where the structured relational data are organized in an abstraction hierarchy that describes the semantics of the content of the data. We show how to cope with some of the challenges presented by partial specification in the case of structured data, that unavoidably results from choosing a particular level of abstraction. Our solution to partial specification is based on a statistical method, called shrinkage. We present results of experiments in the case of learning link-based Naïve Bayes classifiers on a text classification task that (i) demonstrate that the choice of the level of abstraction can impact the performance of the resulting link-based classifiers and (ii) examine the effect of partially specified data.