Knowledge Discovery and modeling in Intelligent Tutoring Systems

知识获取一直是知识系统的主要瓶颈。这对于知识库包括领域本体和问题解决知识的智能教学系统（ITS）来说尤其如此。 为了使信息技术系统在知识发展迅速的领域，如商业和工业环境中蓬勃发展，必须开发新的、易于使用的工具，以获取和维护知识。第一个知识创建问题的根源在于知识库通常必须从头开始手动创建。然而，关键信息已经存在，隐藏在社区和组织内大量现有的文件和数据库中。领域专家需要使其明确，并提供相关的知识，人工导师，使它能够通过能力和解决问题的技能发展指导学习者。然而，在一些领域，解决问题的知识是不容易预先确定的，它的演变。

Knowledge acquisition has always been the major bottleneck for knowledge-based systems (KBS). This is especially true for Intelligent Tutoring Systems (ITS) in which knowledge base includes domain ontology and problem solving knowledge. In order for ITSs to thrive in fields where knowledge evolves rapidly, such as business and industrial settings, new, easy-to-use tools must be developed for knowledge acquisition and maintenance. The first knowledge creation problem finds its source in the fact that the knowledge base usually has to be created manually, from scratch. However, crucial information already exists, hidden in legions of existing documents and databases within communities and organizations. Domain expert are required to make it explicit and provide relevant knowledge to the artificial tutor so that it will be able to guide the learner through competence and problem solving skills development. However, in several domains, problem solving knowledge is not easy to predetermine and it evolves.