@inproceedings{pub5481,
    series = {Lecture Notes in Computer Science, LNCS},
    abstract = {Programming a car's navigation system manually takes time and is error-prone. When the address is not handy, a cumbersome search may start. Changing the destination while driving is even more problematic. Given a modern car's role as an information hub, we argue that an intelligent system could in many cases infer the right destination or have it among the top N suggestions. In this work, we propose a personalized navigation system that is built from three main ingredients: strong user models, knowledge source fusion, and reasoning under uncertainty. We focus on emails as one particular knowledge source, exploring the uncertainties involved when extracting empirical data of email appointments.},
    year = {2011},
    title = {Generating Personalized Destination Suggestions for Automotive Navigation Systems under Uncertainty},
    booktitle = {Proceedings of the 19th International Conference on User Modeling, Adaptation, and Personalization. International Conference on User Modeling, Adaptation, and Personalization (UMAP-2011), 19th, July 11-15, Girona, Spain},
    publisher = {Springer},
    author = {Michael Feld and Martin Theobald and Christoph Stahl and Timm Meiser and Christian Müller},
    keywords = {Knowledge Fusion, Uncertainty Reasoning, Information Extraction},
    url = {http://www.dfki.de/web/forschung/iui/publikationen/renameFileForDownload?filename=uncertainties_posterpaper_submitted.pdf&file_id=uploads_1060}
}