Query Induction for Visual Web Data Extraction

01.06.2005 - 30.09.2008
Research funding project
Previous research on rendering the problem of defining Web wrappers ¿ programs that automatically extract content from Web pages ¿ practicable has resulted in two major approaches to wrapper generation, that of machine learning-based wrapping and that of supervised visual wrapper specification. The goal of this project is to work towards the integration and combination of these two approaches to obtain a framework for defining wrappers that ideally combines the advantages of both. In such a combined approach, machine learning techniques could simplify and speed up the visual wrapper specification process by reducing the number of specification steps to be carried out by the wrapper designer, while learning techniques could be strengthened by new supervised techniques that only become feasible in a visual specification environment.     

People

Project leader

Project personnel

Institute

Grant funds

  • FWF - Österr. Wissenschaftsfonds (National) Austrian Science Fund (FWF)

Research focus

  • Computational Intelligence: 100%

Keywords

GermanEnglish
maschinelles Lernenmachine learning
Web Informations ExtraktionWeb information extraction
induktives Lerneninductive learning

Publications