  Despite extensive research efforts in recent years, computational
argumentation (CA) remains one of the most challenging areas of natural
language processing. The reason for this is the inherent complexity of the
cognitive processes behind human argumentation, which integrate a plethora of
different types of knowledge, ranging from topic-specific facts and common
sense to rhetorical knowledge. The integration of knowledge from such a wide
range in CA requires modeling capabilities far beyond many other natural
language understanding tasks. Existing research on mining, assessing, reasoning
over, and generating arguments largely acknowledges that much more knowledge is
needed to accurately model argumentation computationally. However, a systematic
overview of the types of knowledge introduced in existing CA models is missing,
hindering targeted progress in the field. Adopting the operational definition
of knowledge as any task-relevant normative information not provided as input,
the survey paper at hand fills this gap by (1) proposing a taxonomy of types of
knowledge required in CA tasks, (2) systematizing the large body of CA work
according to the reliance on and exploitation of these knowledge types for the
four main research areas in CA, and (3) outlining and discussing directions for
future research efforts in CA.
