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In this paper I give a short reflection on Knowledge Acquisition as a subfield of AI and Knowledge Engineering over the last 25 years or so. My major message is that knowledge modeling is an underrated but still important method to reduce the complexity problems that arise in constructing knowledge-based applications. Scale - as apparent in the Semantic Web - is another important parameter in recent developments in Knowledge Acquisition: it requires other techniques than those of the 1980s. Natural Language Processing is the most promising way forward, but also the most difficult source of the acquisition of formalized knowledge. I will argue that some of the lessons learned in building knowledge-based systems may carry over to reasoning in the Semantic Web and to knowledge acquisition from natural language web sources.