Argument mining based on a structured database and its usage in an intelligent tutoring environment

摘要

Argumentation theory is a new research area that concerns mainly with reaching a mutually acceptable conclusion using logical reasoning. Argumentation can be defined as a proof of dynamic nature and is considered as an ill-defined domain that typically lacks clear distinctions between “right” and “wrong” answers. Instead, there are often competing reasonable answers. Recently, a number of argument mapping tools have been developed to diagram, articulate, and comprehend different arguments. Despite the fact, these methods are of complementary nature, and the efforts for integrating these tools are missing. The purpose of this paper is threefold: (1) revealing a novel approach for argument representation using a structured relational argument database “RADB”, which has been designed, developed, and implemented in order to represent different argument analyses and diagrams, (2) presenting a classifier agent that utilizes the RADB repository by using different mining techniques in order to retrieve the most relevant arguments to the subject of search, and (3) proposing an agent-based educational environment (ALES) that utilizes the RABD together with the classifier agent to teach argument analysis.