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:
this https URL

本文探讨了从语言图中挖掘常识知识的实用方法，将由语言模式获得的便宜知识转化为昂贵的常识知识，转换为与 ConceptNet 相同的表示方式的 TransOMCS 资源，并证明了语言知识对常识知识的可转移性和提出的方法在数量、新颖性和质量方面的有效性。