Learning ontologies from large text corpora is a well understood task while evolving ontologies dynamically from user-input has rarely been adressed so far. Evolution of ontologies has to deal with vague or incomplete information. Accordingly, the formalism used for knowledge representation must be able to handle this kind of information. Classical logical approaches such as description logics are particularly poor in adressing uncertainty. Ontology evolution may benefit from exploring probabilistic or fuzzy approaches to knowledge representation. In this thesis an approach to evolve and update ontologies is developed which uses explicit and implicit user-input and extends probabilistic approaches to ontology engineering.