The report deals with the largely unexplored field of ontology-driven, knowledge based trend detection by means of text mining, focusing on the development of trend ontologies. The difficulties of trend detection with text mining lie in the ambiguous semantics of natural languages and their various forms, character- istics and dynamics. Due to this it is difficult to formalize knowledge used in trend detection unambiguously and statically. Using ontologies, language com- ponents can be identified and subsequently processed and analyzed regarding their relations to each other. However, due to different languages and specific usages depending on user and application fields, as well as specific trend be- havior in certain application fields, trend ontologies specialized for the intended application are needed. In order to allow the modular development and usage of these different ontologies a standardizing base for trend ontologies is needed. This base can be realized as meta ontology and its development is the central aspect of the report.
