For over 50 years researchers and practitioners have searched for ways to elicit and formalize expert knowledge to support AI applications. Expert systems and knowledge bases were all results of these efforts. The initial efforts on knowledge bases were focused on defining a domain and task intensionally with rather complex ontologies. The increasing complexity of knowledge and knowledge-based systems eventually led to the development of knowledge engineering methodologies. Knowledge graphs, in contrast to the traditional knowledge bases, represent knowledge more extensionally with a very large set of explicit statements and rather simpler and smaller ontologies. This paradigm change calls for a new take on knowledge engineering that focuses on the curation of ABox statements. In this paper, we introduce various aspects of the knowledge graphs lifecycle namely creation, hosting, curation and deployment. We define each task, give example approaches from the literature and explain our approach with a running example. Additionally, we present the German Tourism Knowledge Graph that is being implemented with our methodology.