BibSonomy publications for /user/nepomuk/04Thu Jul 24 00:24:34 CEST 2008AAAI Spring Symposium on AI Meets Business Rules and Process Management, Stanford Univ.99-104Business Process Modeling, Task Management, and the Semantic Link200804 2008 SAP WP3 businessprocess from:uvriss lang:en semanticwebservices webservice Wed Jun 18 21:26:52 CEST 200804Presentation of NEPOMUK and Case Studies at Oil Industry Norway Semantic Web Days200804 2008 dfki from:leobard nepomuk wp2 Presentation of case studies of successfull Semantic Web use, NEPOMUK is one of them. Audience are Oil Industry and Defence Industry IT Executives.Tue May 27 15:37:17 CEST 2008Conference TalkAPRSemantic Web zum Anfassen200804 2008 FZI WP1 from:heikohaller lang:de nepomuk 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.Wed Jun 13 10:37:56 CEST 200712th International Conference on Applications of Natural Language to Information Systems (NLDB) June 27-29, 2007, CNAM, Paris, FranceLecture Notes in Computer ScienceDomain Relevance on Term Weighting4592200704 2006 dfki from:biberburg wp2 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 \cite{DBLP: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.