Contextual commonsense knowledge acquisition from social content by crowd-sourcing explanations

Contextual knowledge is essential in answering questions given specific observations. While recent approaches to building commonsense knowledge bases via text mining and/or crowdsourcing are successful, contextual knowledge is largely missing. To address this gap, this paper presents SocialExplain, a novel approach to acquiring contextual commonsense knowledge from explanations of social content. The acquisition process is broken into two cognitively simple tasks: to identify contextual clues from the given social content, and to explain the content with the clues. An experiment was conducted to show that multiple pieces of contextual commonsense knowledge can be identified from a small number of tweets. Online users verified that 92.45% of the acquired sentences are good, and 95.92% are new sentences compared with existing crowd-sourced commonsense knowledge bases.