Supported by ALVIS, KnowledgeWeb, Network of Excellence Open Source Search, SEKT, PASCAL Network of Excellence and SmartWeb

Abstract

The emerging world of search we see is one which makes increasing use of information extraction, gradually blends in semantic web technology and peer to peer systems, and uses grid computing as part of resources for information extraction and learning. This workshop aims at exploring the theory and application of machine learning in this context for the internet, intranets, the emerging semantic web and peer to peer search. We are happy to see that this workshop succeeded in attracting a large number of high quality paper submissions, 8 of which were selected by the program committee. Besides this, three invited speakers have agreed to complement the paper presentations. In his invited talk Large Margin Methods in Information Extraction and Content Categorization, Thomas Hofmann gives insights on using Support Vector Machines for predicting structured output variables. The papers A Web-based kernel Function for Matching Short Text Snippets and A Semantic Kernel to classify Texts with very few Training Examples also contribute to the field of kernel methods. In using formalized background knowledge, the latter seamlessly matches with the contribution Learning Word-to-Concept Mappings for Automatic Text Classification. The task of automated knowledge markup for the Semantic Web is addressed by means of machine learning methods in th