Knowledge acquisition is usually the first step in building ontologies. On the one hand, knowledge is typically implicitly contained in large collections of unstructured documents. Therefore it is extremely troublesome to manually identify relevant concepts. On the other hand, users are often not fully satisfied with the results of automated state-of-the-art ontology learning techniques. In this paper we present a technique for large-scale Knowledge Acquisition supported Semi-automated Ontology building (KASO) and a corresponding software system. By applying KASO and using this software, users are able to bootstrap the process of building high quality ontologies by automatically acquiring concepts from large-scale document collections and to make use of traditional knowledge acquisition approaches to refine and organize the machine-generated concepts. Evaluation studies and user experiences indicate the applicability of KASO in bootstrapping ontology construction.