利用智能引导和KDML增强可拓模型人机建模能力研究

Research on enhancing the human-machine modeling ability for an extension model using the intelligent guide and KDML

为了利用计算机协助人们建立待求解问题的可拓模型,必须让计算机识别用自然语言描述的问题,而且要理解问题的含义,这是相当困难的任务。本文提出利用人机界面Agent的智能引导并结合知网(How Net)中的知识系统描述语言(KDML),增强计算机语义处理能力的方法。以求职问题为实践的结果说明了方法的有效性。由于KDML有较强的表示语义信息的作用,通过人机交互也能减轻计算机自然语言理解的困难。因此该方法能将自然语言描述的待求解问题的目标和条件进行分离和形式化,使计算机更有效地建立待求解问题的可拓模型。

When using a computer to establish an extension model for solving problems,the computer must be able to recognize problems described in natural languages,and,in particular,must understand the meaning of the problems. This is a very difficult task. Knowledge database mark-up language( KDML) in How Net has a strong semantic information expression function. Through human-machine interaction,it can also reduce the difficulty that computers encounter when understanding natural language. A method to enhance the computer ' s semantic processing ability,based on the human-machine interface agent 's intelligent guide and KDML,is proposed. The goals and conditions of the problems to be solved described in natural language are separated and formalized,making the computer establish the problem-solving extension model more effectively.