Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (8): 149-153.DOI: 10.3778/j.issn.1002-8331.1610-0224

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Modular thinking’s application in large scale ontology matching

CHEN Heng1,2, LI Guanyu2, CHEN Xinying2,3   

  1. 1.School of Software, Dalian University of Foreign Languages, Dalian, Liaoning 116044, China
    2.Faculty of Information Science & Technology, Dalian Maritime University, Dalian, Liaoning 116026, China
    3.School of Software Technology, Dalian Jiaotong University, Dalian, Liaoning 116028, China
  • Online:2017-04-15 Published:2017-04-28


陈  恒1,2,李冠宇2,陈鑫影2,3   

  1. 1.大连外国语大学 软件学院,辽宁 大连 116044
    2.大连海事大学 信息科学技术学院,辽宁 大连 116026
    3.大连交通大学 软件学院,辽宁 大连 116028

Abstract: Ontology matching is a way to solve semantic heterogeneity and to realize the sharing and reuse of ontology. But the size of ontology is increasing, in order to reduce match space, a modularization based ontology matching framework is proposed. Firstly, ontology is transformed into a concept map using preprocessed algorithm. Secondly, the ROCK clustering algorithm is improved, and the concept map is divided into several high cohesion and low coupling concept blocks using this algorithm. Lastly, according to the Tversky model, block match degree is calculated by the father concept, son concept, brother concept and intension. And important concepts of block are signed, n∶m matching is performed. It is observed that the proposed ontology matching framework can balance the block size, improve match efficiency.

Key words: ontology matching, semantic heterogeneity, concept block, concept clustering, Tversky model

摘要: 本体匹配是解决语义异构,实现本体共享与重用的一种方法。但本体规模越来越大,为降低匹配空间,提出一种基于模块化思想的本体匹配框架。首先,使用预处理算法将待匹配本体转换成概念图;然后,改进了ROCK聚类算法,并使用该算法将概念图划分成若干高内聚低耦合的概念块;最后,根据Tversky模型,从概念的父、子、兄弟以及内涵4个方面计算块的匹配度,并标记块的重要概念,进行n∶m匹配。实验结果表明,提出的本体匹配框架能够均衡本体分块大小,提高匹配效率。

关键词: 本体匹配, 语义异构, 概念块, 概念聚类, Tversky模型