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To make machine exhibit human-like abilities in the domains like robotics andconversation, social commonsense knowledge (SCK), i.e., common sense aboutsocial contexts and social roles, is absolutely necessarily. Therefor, ourultimate goal is to acquire large-scale SCK to support much more intelligentapplications. Before that, we need to know clearly what is SCK and how torepresent it, since automatic information processing requires data andknowledge are organized in structured and semantically related ways. For thisreason, in this paper, we identify and formalize three basic types of SCK basedon first-order theory. Firstly, we identify and formalize theinterrelationships, such as having-role and having-social_relation, amongsocial contexts, roles and players from the perspective of considering bothcontexts and roles as first-order citizens and not generating role instances.Secondly, we provide a four level structure to identify and formalize theintrinsic information, such as events and desires, of social contexts, rolesand players, and illustrate the way of harvesting the intrinsic information ofsocial contexts and roles from the exhibition of players in concrete contexts.And thirdly, enlightened by some observations of actual contexts, we furtherintroduce and formalize the embedding of social contexts, and depict the way ofexcavating the intrinsic information of social contexts and roles from theembedded smaller and simpler contexts. The results of this paper lay thefoundation not only for formalizing much more complex SCK but also foracquiring these three basic types of SCK.

A Logical Model for Supporting Social Commonsense Knowledge Acquisition