gIBIS: a hypertext tool for exploratory policy discussion
ACM Transactions on Information Systems (TOIS)
Representing the structure of a legal argument
ICAIL '89 Proceedings of the 2nd international conference on Artificial intelligence and law
PHIDIAS: integrating CAD graphics into dynamic hypertext
Hypertext: concepts, systems and applications
Supporting collaborative writing of hyperdocuments in SEPIA
CSCW '92 Proceedings of the 1992 ACM conference on Computer-supported cooperative work
Supporting knowledge-base evolution with incremental formalization
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Spatial hypertext and the practice of information triage
HYPERTEXT '97 Proceedings of the eighth ACM conference on Hypertext
Making argumentation serve design
Human-Computer Interaction
Cohere: Towards Web 2.0 Argumentation
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Exploiting background knowledge for knowledge-intensive subgroup discovery
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The dicode workbench: a flexible framework for the integration of information and web services
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
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Argumentation support systems have yet to deliver their full potential to teams striving for informed sense and decision making. These systems 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 an incremental formalization of argumentative discourses, as well as the integration of argumentation support systems with data mining services. The overall aim of our approach is to semantically augment argumentative discourses, while also exploiting the synergy between tools supporting machine and human intelligence.