Supporting case acquisition and labelling in the context of web mining

Abstract

Case acquisition and labelling are important bottlenecks for predictive data mining. In the web context, a cascade of supporting tech- niques can be used, from general ones such as user interfaces, through filtering based on keyword frequency, to web-specific techniques exploit- ing public search engines. We show how a synergistic application of mul- tiple techniques can be helpful in obtaining and pre-processing textual data, in particular for ILP-based web mining. The (two-fold) learning task itself consist in construction and disambiguation of categorisation rules, which are to process the results returned by web search engines.