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Sources of commonsense knowledge support applications in natural languageunderstanding, computer vision, and knowledge graphs. Given theircomplementarity, their integration is desired. Yet, their different foci,modeling approaches, and sparse overlap make integration difficult. In thispaper, we consolidate commonsense knowledge by following five principles, whichwe apply to combine seven key sources into a first integrated CommonSenseKnowledge Graph (CSKG). We analyze CSKG and its various text and graphembeddings, showing that CSKG is well-connected and that its embeddings providea useful entry point to the graph. We demonstrate how CSKG can provide evidencefor generalizable downstream reasoning and for pre-training of language models.CSKG and all its embeddings are made publicly available to support furtherresearch on commonsense knowledge integration and reasoning.

CSKG: The CommonSense Knowledge Graph