TABLE OF CONTENTS Johannes F rnkranz
TU Darmstadt, Knowledge Engineering Group
| Abstract | The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to Web data and documents. This chapter provides a brief overview of web mining techniques and research areas, most notably hypertext classification, wrapper induction, recommender systems and web usage mining. |
| Keywords: | web mining, content mining, structure mining, usage mining, text classification, hypertext classification, information extraction, wrapper induction, collaborative filtering, recommender systems, Semantic Web |
The advent of the World-Wide Web (WWW) (Berners-Lee, Cailliau, Loutonen, Nielsen & Secret, 1994) has overwhelmed home computer users with an enormous flood of information. To almost any topic one can think of, one can find pieces of information that are made available by other internet citizens, ranging from individual users that post an inventory of their record collection, to major companies that do business over the Web.
To be able to cope with the abundance of available information, users of the Web need assistance of intelligent software agents (often called softbots) for finding, sorting, and filtering the available information (Etzioni, 1996; Kozierok and Maes, 1993). Beyond search engines, which are already commonly used, research concentrates on the development of agents that are general, high-level interfaces to the Web (Etzioni, 1994; F rnkranz et al., 2002), programs for filtering and sorting e-mail...
