  Commonsense knowledge is essential for many AI applications, including those
in natural language processing, visual processing, and planning. Consequently,
many sources that include commonsense knowledge have been designed and
constructed over the past decades. Recently, the focus has been on large
text-based sources, which facilitate easier integration with neural (language)
models and application on textual tasks, typically at the expense of the
semantics of the sources. Such practice prevents the harmonization of these
sources, understanding their coverage and gaps, and may hinder the semantic
alignment of their knowledge with downstream tasks. Efforts to consolidate
commonsense knowledge have yielded partial success, but provide no clear path
towards a comprehensive consolidation of existing commonsense knowledge.
  The ambition of this paper is to organize these sources around a common set
of dimensions of commonsense knowledge. For this purpose, we survey a wide
range of popular commonsense sources with a special focus on their relations.
We consolidate these relations into 13 knowledge dimensions, each abstracting
over more specific relations found in sources. This consolidation allows us to
unify the separate sources and to compute indications of their coverage,
overlap, and gaps with respect to the knowledge dimensions. Moreover, we
analyze the impact of each dimension on downstream reasoning tasks that require
commonsense knowledge, observing that the temporal and desire/goal dimensions
are very beneficial for reasoning on current downstream tasks, while
distinctness and lexical knowledge have little impact. These results reveal
focus towards some dimensions in current evaluation, and potential neglect of
others.
