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Natural language processing relies heavily on resources. Most common usage scenarios include using the resources for automated lexical tagging or named entity recognition. Also manually annotated language resources are used for benchmarking of new automated approaches. To allow any processing on a large scale and considering the complexity of natural language (words can have multiple meanings within the same general context) the resources have to be quite large. In this paper we focus on lexical resources in ontology form.