集成本体和CBR的数据挖掘建模技术及其在工艺规划中应用研究

摘要

利用数据挖掘技术解决智能CAPP系统知识获取问题时面临着应用门槛高的严重问题.为了克服该问题,使数据挖掘技术能方便地被普通用户应用,提出了集成本体和CBR技术进行数据挖掘建模从而控制数据挖掘自动化进行的方法.首先建立了基于本体语义的工艺规划数据挖掘事例库,然后确立了本体语义理解型推理机制和相应的概念相似度算法,并初步探讨了数据挖掘事例的综合评价方法及修改技术.最后,以典型的机加工零件为应用实例对建议方法进行验证.试验证明本文建议的方法通过智能化建模控制数据挖掘过程自动化进行,克服了传统数据挖掘建模需要领域专家和知识工程师协作的局限,显著降低了数据挖掘技术的使用门槛.%While data mining technology is applied to solve the knowledge acquisition of intelligent CAPP (Computer aided process planning) system,a serious problem is faced that it is hard for common users to use data mining.In order to overcome the problem,a method of data mining modeling by integrating ontology and CBR to control the data mining process intelligently is proposed.Firstly,a case base for data mining of process planning is built based on the ontological semantics;secondly,a reasoning mechanism in type of semantic understanding is established,as well as comprehensive evaluation and modification technique for data mining case is discussed.At lastly,the process planning of a typical mechanical part is taken for an example to test the present method.The experiments show that present method breaks the limit of traditional data mining,which has to be executed through the collaboration of domain experts and knowledge engineers,and is able to execute intelligent data mining.As a result,the threshold of its application is dramatically reduced.