Argument mining based on a structured database and its usage in an intelligent tutoring environment
Argument mining based on a structured database and its usage in an intelligent tutoring environment
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DOI：
10.1007/s10115-010-0371-3
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发表时间：
2010
影响因子：
2.7
通讯作者：
Safia Abbas;Hajime Sawamura
中科院分区：
文献类型：
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作者：
Safia Abbas;Hajime Sawamura
论证理论是一个新兴的研究领域，主要研究如何通过逻辑推理得出一个双方都能接受的结论。论证可以被定义为动态性质的证明，被认为是一个定义不清的领域，通常缺乏“正确”和“错误”答案之间的明确区分。相反，通常会有相互竞争的合理答案。最近，已经开发了许多论证映射工具来绘制、表达和理解不同的论证。尽管如此，这些方法具有互补的性质，并且缺少集成这些工具的努力。本文的目的有三个：(1)揭示了一种使用结构化关系参数数据库“RADB”来表示参数的新方法，该数据库是为了表示不同的参数分析和图表而设计、开发和实现的；(2)提出了一个分类器代理，该代理通过使用不同的挖掘技术来利用RADB存储库，以便检索与搜索主题最相关的参数；(3)提出了一种基于智能体的教学环境（ALES），该环境利用RABD和分类器智能体进行论证分析教学。
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.