Kugel, Felix and Haller, Heiko Semantic Web zum Anfassen 2008 APR Conference Talk Die formale Modellierung von Fachdomänen auf Basis von Webstandards wurde ursprünglich von einer Community um Tim Berners-Lee (W3C) vorangetrieben. Es werden Beispiele vorgestellt sowie Startpunkte für die Suche nach Ontologien gegeben, die sich für EAI via SOA und Web anbieten. Im Anschluss werden Tools gezeigt, die die einschlägigen Standards (RDF, OWL, SPARQL) auf Java-Ebene transportieren. \urlhttp://www.oio.de/m/konf/jax2008/semantic_web_zum_anfassen_jax_2008.pdf 04 2008 FZI WP1 from:heikohaller lang:de nepomuk Riss, Uwe V. and Weber, Ingo and Grebner, Olaf Hinkelmann, K. Business Process Modeling, Task Management, and the Semantic Link AAAI Spring Symposium on AI Meets Business Rules and Process Management, Stanford Univ. American Association for Artificial Intelligence, Menlo Park, Calif. 2008 99-104 04 2008 SAP WP3 businessprocess from:uvriss lang:en semanticwebservices webservice Sauermann, Leo Presentation of NEPOMUK and Case Studies at Oil Industry Norway Semantic Web Days 2008 04 Presentation of case studies of successfull Semantic Web use, NEPOMUK is one of them. Audience are Oil Industry and Defence Industry IT Executives. http://leobard.twoday.net/stories/4882026/ 04 2008 dfki from:leobard nepomuk wp2 Brunzel, Marko and Spiliopoulou, Myra Domain Relevance on Term Weighting 12th International Conference on Applications of Natural Language to Information Systems (NLDB) June 27-29, 2007, CNAM, Paris, France Springer 2007 4592 Lecture Notes in Computer Science The TFxIDF term weighting scheme is the standard approach on vectorization of textual data. For a data set where textual data stemming from web document structure is to be vectorized \citeDBLP:conf/kdxd/BrunzelS06 the need for a enhanced term weighting scheme arose. In this publication we introduce a term weighting scheme which improves the behavior compared to the traditional TFxIDF scheme by adding a component which is based on the linguistically inspired notion of domain relevance. Domain relevance measures the degree to which a term is regarded as more relevant within a data set compared to a reference data set. By means of this external component a potential weakness of TFxIDF on non standard distributed data sets is overcome. This weighting scheme favours domain relevant terms, which can be regarded as more useful in settings where the clustering is performed to be consumed by an human supervisor e.g for semi-automatic ontology learning. 04 2006 dfki from:biberburg wp2