Using semantic types to formalize and augment complex argumentative discourses

Abstract

Argumentation support systems currently on the Web have yet to deliver their full potential to teams striving for informed sense and decision making. Today's widely used systems such as discussion forums do not support formalization and are poorly integrated in the environment of multidisciplinary teams that collaborate in data intensive and cognitive complex settings, such as those involving DNA analysis, marketing or drug testing research. Such teams use on a daily basis tools to collect big amounts of required data as well as sophisticated data mining tools to uncover patterns in the collected data. However, these tools do not interoperate with argumentation support systems. In this paper, we present an approach to support collaboration which exploits a range of semantic types to enable formalization of argumentative discourses. Semantic types also enable the integration of argumentation support systems with data mining services to further augment collaboration and decision making in the above teams. An evaluation of the approach shows that the platform enables stakeholders to make better, more informed and quicker decisions, by displaying the aggregated information according to their needs. The overall idea of our approach builds on the exploitation of the synergy between tools supporting machine and human intelligence.