  Commonsense knowledge acquisition is a key problem for artificial
intelligence. Conventional methods of acquiring commonsense knowledge generally
require laborious and costly human annotations, which are not feasible on a
large scale. In this paper, we explore a practical way of mining commonsense
knowledge from linguistic graphs, with the goal of transferring cheap knowledge
obtained with linguistic patterns into expensive commonsense knowledge. The
result is a conversion of ASER [Zhang et al., 2020], a large-scale selectional
preference knowledge resource, into TransOMCS, of the same representation as
ConceptNet [Liu and Singh, 2004] but two orders of magnitude larger.
Experimental results demonstrate the transferability of linguistic knowledge to
commonsense knowledge and the effectiveness of the proposed approach in terms
of quantity, novelty, and quality. TransOMCS is publicly available at:
https://github.com/HKUST-KnowComp/TransOMCS.
