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Automatic methods for grapheme-to-phoneme (G2P) and phoneme-to-grapheme (P2G) conversion have become very popular in recent years. Their performance has improved considerably, while at the same time these developments required less input from expert lexicographers. Continuing in this tradition we will present in this paper a data-driven, language-independent approach called MASSIVE with which it is possible to create efficient online modules for automatic symbol mapping. Our framework is solely based on statistical methods for training and run-time and has been optimized for P2G conversion in the context of spoken inquiries to the Semantic Web, an issue researched in the SmartWeb project. MASSIVE systems can be trained using a pronunciation lexicon, the output of a phone recognizer or any other suitable set of corresponding symbol strings. Successful tests have been performed on German and English data sets.