Abstract: Knowledge Management software is software that integrates. Existing Data sources, process flows, application features from office appliances have to be brought together. There are different standards, consisting of data formats and communication protocols, that address this issue. The WWW and Semantic Web are designed to work on a worldwide scale and define those standards. We transfer the web standards to the desktop szenario, a vision we call Semantic Desktop – a Semantic Web enhanced desktop environment. Central is the idea of taking know-how from the Semantic Web to tackle personal information management. Existing desktop applications (email client, browser, office applications) are integrated, the semantic glue between them expressed using ontologies. We also present the www.gnowsis.org open source project by the DFKI that realizes parts of this vision. It is based on a Semantic Web Server running as desktop service. It was used in experiments and research projects and allows others to experiment. Knowledge management applications can be built on top of it, reducing the implementation cost. 1

This thesis focuses on computational discourse models for collaboratively edited corpora. Due to the exponential growth rate and significant stylistic and content variations of collaboratively edited corpora, models based on professionally edited texts are incapable of processing the new data effectively. For these methods to succeed, one challenge is to preserve the local coherence as well as global consistence. We explore two corpus-based methods for processing collaboratively edited corpora, which effectively model and optimize the consistence of user generated text. The first method addresses the task of inserting new information into existing texts. In particular, we wish to determine the best location in a text for a given piece of new information. We present an online ranking model which exploits this hierarchical structure – representationally in its features and algorithmically in its learning procedure. When tested on a corpus of Wikipedia articles, our hierarchically informed model predicts the correct insertion paragraph more accurately than baseline methods. The second method concerns inducing a common structure across multiple articles in similar domains to