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
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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.
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.
引文
[1]王军强,周雪明,郭银洲.可扩展制造执行系统软件体系结构设计与实现[J].计算机集成制造系统,2014,20(5):1035-1050Wang J Q,Zhou X M,Guo Y Z.Design and implementation of software architecture for extensible manufacturing execution system[J].Computer Integrated Manufacturing Systems,2014,20(5):1035-1050(in Chinese)
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[8]卢其福,解建仓,贺鑫焱,等.基于CBR的防汛知识获取研究[J].水利科技与经济,2008,14(2):87-91Lu Q F,Xie J C,He X Y,et al.Research on the flood control knowledge acquisition based on CBR[J].Water Conservancy Science and Technology and Economy,2008,14(2):87-91(in Chinese)
[9]Vieira M A,Formaggio A R,RennóC D,et al.Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas[J].Remote Sensing of Environment,2012,123:553-562
[10]Guo Y,Peng Y H,Hu J.Research on high creative application of case-based reasoning system on engineering design[J].Computers in Industry,2013,64(1):90-103
[11]Sánchez D,Batet M.A semantic similarity method based on information content exploiting multiple ontologies[J].Expert Systems with Applications,2013,40(4):1393-1399
[12]Jiang J J,Conrath D W.Semantic similarity based on corpus statistics and lexical taxonomy[C]//Proceedings of International Conference Research on Computational Linguistics.Taiwan,1997
[13]Kobti Z,Chen D,Baljeu A.A domain ontology model for mould design automation[M]//Farzindar A,Ke2elj V.Advances in Artificial Intelligence.Berlin Heidelberg:Springer,2010:336-339
[14]Sánchez D,Batet M,Isern D.Ontology-based information content computation[J].Knowledge-Based Systems,2011,24(2):297-303
[15]Guo Y,Hu J,Peng Y H.A CBR system for injection mould design based on ontology:a case study[J].Computer-Aided Design,2012,44(6):496-508
[2]Wirth R,Hipp J.CRISP-DM:towards a standard process model for data mining[C]//Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining.Manchester,UK,2000:29-39
[3]Kalousis A,Theoharis T.NOEMON:design,implementation and Performance results of an intelligent assistant for classifier selection[J].Intelligent Data Analysis,1999,3(5):319-337
[4]Broadley M R,White P J,Hammond J P,et al.Zinc in plants[J].New Phytologist,2007,173(4):677-702
[5]Lin F Y,Mc Clean S.A data mining approach to the prediction of corporate failure[J].Knowledge-Based System,2001,14(3-4):189-195
[6]高雅田.基于MAS的数据挖掘模型自动选择方法研究[D].黑龙江大庆:东北石油大学,2011Gao Y T.Research on automatic selection methods of data mining models based on MAS[D].Heilongjiang Daqing:Northeast Petroleum University,2011(in Chinese)
[7]Xu X,Wang L H,Newman S T.Computer-aided process planning-A critical review of recent developments and future trends[J].International Journal of Computer Integrated Manufacturing,2011,24(1):1-31
[8]卢其福,解建仓,贺鑫焱,等.基于CBR的防汛知识获取研究[J].水利科技与经济,2008,14(2):87-91Lu Q F,Xie J C,He X Y,et al.Research on the flood control knowledge acquisition based on CBR[J].Water Conservancy Science and Technology and Economy,2008,14(2):87-91(in Chinese)
[9]Vieira M A,Formaggio A R,RennóC D,et al.Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas[J].Remote Sensing of Environment,2012,123:553-562
[10]Guo Y,Peng Y H,Hu J.Research on high creative application of case-based reasoning system on engineering design[J].Computers in Industry,2013,64(1):90-103
[11]Sánchez D,Batet M.A semantic similarity method based on information content exploiting multiple ontologies[J].Expert Systems with Applications,2013,40(4):1393-1399
[12]Jiang J J,Conrath D W.Semantic similarity based on corpus statistics and lexical taxonomy[C]//Proceedings of International Conference Research on Computational Linguistics.Taiwan,1997
[13]Kobti Z,Chen D,Baljeu A.A domain ontology model for mould design automation[M]//Farzindar A,Ke2elj V.Advances in Artificial Intelligence.Berlin Heidelberg:Springer,2010:336-339
[14]Sánchez D,Batet M,Isern D.Ontology-based information content computation[J].Knowledge-Based Systems,2011,24(2):297-303
[15]Guo Y,Hu J,Peng Y H.A CBR system for injection mould design based on ontology:a case study[J].Computer-Aided Design,2012,44(6):496-508