Commonsense reasoning is an important aspect of building robust AI systems and isreceiving significant attention in the natural language understanding, computer vision, andknowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths, and weaknesses. In this paper, we listrepresentative sources and their properties. Based on this survey, we propose principlesand a representation model in order to consolidate them into a CommonSense KnowledgeGraph (CSKG). We apply this approach to consolidate seven separate sources into a firstintegrated CSKG. We present statistics of CSKG, present initial investigations of its utilityon four QA datasets, and list learned lessons