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Case acquisition and labelling are important bottlenecks for predictive data mining. In the web context, a cascade of supporting techniques can be used, from general ones such as user interfaces, through filtering based on keyword frequency, to web-specific techniques exploiting public search engines. We show how a synergistic application of multiple 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.

Supporting Case Acquisition and Labelling in the Context of Web Mining