Dimensions of Commonsense Knowledge
  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 to textual tasks, typically at the expense of the
semantics of the sources and their harmonization. Efforts to consolidate
commonsense knowledge have yielded partial success, with no clear path towards
a comprehensive solution. We aim to organize these sources around a common set
of dimensions of commonsense knowledge. We survey a wide range of popular
commonsense sources with a special focus on their relations. We consolidate
these relations into 13 knowledge dimensions. 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
preferences for some dimensions in current evaluation, and potential neglect of
others.
