A Logical Model for Supporting Social Commonsense Knowledge Acquisition
  To make machine exhibit human-like abilities in the domains like robotics and
conversation, social commonsense knowledge (SCK), i.e., common sense about
social contexts and social roles, is absolutely necessarily. Therefor, our
ultimate goal is to acquire large-scale SCK to support much more intelligent
applications. Before that, we need to know clearly what is SCK and how to
represent it, since automatic information processing requires data and
knowledge are organized in structured and semantically related ways. For this
reason, in this paper, we identify and formalize three basic types of SCK based
on first-order theory. Firstly, we identify and formalize the
interrelationships, such as having-role and having-social_relation, among
social contexts, roles and players from the perspective of considering both
contexts and roles as first-order citizens and not generating role instances.
Secondly, we provide a four level structure to identify and formalize the
intrinsic information, such as events and desires, of social contexts, roles
and players, and illustrate the way of harvesting the intrinsic information of
social contexts and roles from the exhibition of players in concrete contexts.
And thirdly, enlightened by some observations of actual contexts, we further
introduce and formalize the embedding of social contexts, and depict the way of
excavating the intrinsic information of social contexts and roles from the
embedded smaller and simpler contexts. The results of this paper lay the
foundation not only for formalizing much more complex SCK but also for
acquiring these three basic types of SCK.
