Towards Web-based Mass Argumentation in Natural Language

Within the artificial intelligence community, argumentation has been studied for quite some years now. Despite progress, the field has not yet succeeded in creating support tools that members of the public could use to contribute their views to discussions of public policy. One important reason for that is that the input statements of participants in policy-making discussions are put forward in natural language, while translating the statements into the formal models used by argumentation scientists is cumbersome. These formal models can be used to automatically reason with, query, or transmit domain knowledge using web-based technologies. Making this knowledge explicit, formal, and expressed in a language which a machine can process is a labour, time, and knowledge intensive task. To make such translation and it requires expertise that most participants in policy-making debates do not have. In this paper we describe an approach with which we aim at contributing to a solution of this knowledge acquisition bottle-neck. We propose a novel, integrated methodology and framework which adopts and adapts existing technologies. We use semantic wikis which support mass, collaborative, distributive, dynamic knowledge acquisition. In particular, ACEWiki incorporates NLP tools, enabling linguistically competent users to enter their knowledge in natural language, while yielding a logical form that is suitable for automated processing. In the paper we will explain how we can extend the ACEWiki and augment it with argumentation tools which elicit knowledge from users, making implicit information explicit, and generate subsets of consistent knowledge bases from inconsistent knowledge bases. To a set of consistent propositions, we can apply automated reasoners, allowing users to draw inferences and make queries. The methodology and framework take a fragmentary, incremental development approach to knowledge acquisition in complex domains.