Open debates include so many arguments that sound decision making exceeds cognitive capabilities of the interested public or responsible experts. Until now, argument mining approaches typically map from a closed set of given texts into a formal argumentation model. However, this does not fully cater to the nature of open, ongoing debates because of the following challenges:

(C1) Following a Continuous Debate: Participants in open, mass debates continuously introduce new arguments with novel aspects relevant to the debate topic. This leads to brittleness of state-of-the-art argument extractors, which are trained once and for all time.

(C2) Dealing with Incomplete Arguments: Textual arguments are often incomplete because the participants in the debate can understand them based on common background or shared knowledge. Hence argument structures which are identified by current argument mining methods are often incomplete.

(C3) Establishing Open Knowledge for Argumentation: Interpreting and understanding textual arguments requires additional facts and common knowledge which are often absent in existing knowledge graphs.

Our project “Open Argument Mining” investigates computational methods that i) continuously improve their capability to recognize arguments in ongoing debates, ii) align incomplete arguments with previous arguments and enrich them with automatically acquired background knowledge, and iii) continuously extend semantic knowledge bases with information required to understand arguments. We intend to achieve this by combining the two research fields argument mining and knowledge graph construction.