基于粗概念格模型的电子商务领域本体的构建研究

J4 ›› 2014, Vol. 36 ›› Issue (03): 530-535.

基于粗概念格模型的电子商务领域本体的构建研究

徐红升，张瑞玲

（洛阳师范学院信息技术学院，河南洛阳 471022）

收稿日期:2012-08-29 修回日期:2012-12-26 出版日期:2014-03-25 发布日期:2014-03-27
基金资助:
国家自然科学基金资助项目(61050004,61272015）；河南省教育厅科学技术研究重点项目(13B520155)

Research of domain ontology construction
in e-business based on rough concept lattice model

XU Hongsheng，ZHANG Ruiling

（College of Information Technology,Luoyang Normal University,Luoyang 471022,China）

Received:2012-08-29 Revised:2012-12-26 Online:2014-03-25 Published:2014-03-27

摘要/Abstract

摘要：

以构建电子商务系统中的本体为出发点，分析现有的本体构建技术中存在的缺陷。针对这些不足，综合考虑变精度粗糙集模型和形式概念分析的相关理论，提出基于粗概念格模型来构建本体。将变精度粗糙集的β选取算法和可辨识矩阵属性约简算法进行了改进，使β上、下分布的约简方法适用于形式背景的约简，从而提出基于变精度粗糙集的概念格约减算法；然后计算语义概念相似度，并以联合国标准产品与服务分类代码的本体元模型为核心本体，结合领域专家知识，建立电子商务领域本体模型。实验表明了粗概念格构建本体的高效性。

关键词: 本体, 变精度粗糙集, 概念格, 属性约简

Abstract:

The defects of existing ontology construction technology are analyzed in order to construct the ontology model of the ecommerce system. The model of ontology construction based on rough concept lattice is proposed for the sake of making up these defects by integrating the theory of variable precision rough set (VPRS) model and formal concept analysis. The method of βupper and lower distribution reduction is used to reduce formal context by improving the two algorithms of βvalues select and attribute reduction based on discernibility matrix in VPRS. Therefore, the paper proposes the reduction algorithm of concept lattices based on VPRS. After calculating the semantic similarity of concepts, the domain ontology model of ebusiness is built combined with knowledge of domain expert and original ontology model of the United Nations Standard Products and Services Classification Code by way of core ontology. Experiments show that the efficiency of building ontology based on rough concept lattice is higher than FCA.

Key words: ontology;variable precision rough set;concept lattice;attribute reduction

徐红升，张瑞玲. 基于粗概念格模型的电子商务领域本体的构建研究[J]. J4, 2014, 36(03): 530-535.

XU Hongsheng，ZHANG Ruiling. Research of domain ontology construction
in e-business based on rough concept lattice model [J]. J4, 2014, 36(03): 530-535.

参考文献

［1］Dei W, Yi M. An approach of personalization for electronic commerce websites based on ontology［C］∥Proc of the 7th IFIP International Conference on eBusiness, eServices and eSociety, 2007:491498.
［2］Richards D. Using concept lattices for requirements reconciliation［C］∥Proc of the 2nd International Conference on Formal Concept Analysis, 2004:402409.
［3］Ganter B, Wille R. Formal concept analysis:Mathematical foundations［M］. Berlin:Springer Verlag, 1999.
［4］Li Jing. The research application of ontology in literature retrieval system ［M］. Beijing:Publishing House of Beijing Library, 2005. (in Chinese)
［5］Shamsfard M， Barforoush A A. Learning ontologies from natural language texts ［J］. International Journal of HumanComputer Studies,2004,60(1):1763.
［6］Navigli R, Velardi P, Gangemi A. Ontology learning and its application to automated terminology translation［J］.IEEE Intelligent Systems,2003,18(1):2231.
［7］Maedch A.Ontology learning for the semantic web［M］. Boston:Kluwer Academic Publishers,2002.
［8］Volz R,Oberle D,Staab S,et al. OntoLIFT prototype［C］∥Proc of IST Project 200133052 WonderWeb Deliverable 11.2003:1.
［9］Liang Jian,Wang Huilin.Research on ontology learning from text ［J］. Journal of Intelligence Theory and Practice, 2007，30(1):112115. (in Chinese)
［10］Du Xiaoyong, Li Man,Wang Shan. Research on ontology learning ［J］. Journal of Software, 2006 ,17(9):18371847. (in Chinese)
［11］Xu Hongsheng, Shen Xiajiong, Liu Zongtian. Construction and presentation of ontology on semantic web based on formal concept［J］. Journal of Computer Science, 2007,34(2)：171174. (in Chinese)
［12］Yao Y Y.A comparative study of formal concept analysis and rough set theory in data analysis:Rough sets and current trends in computing［C］∥Proc of the 3rd International Conference,2004:5968.
［13］Beynon M. Reducts within the variable precision rough sets model:A further investigation［J］. European Journal of Operational Research, 2001,134(3):592605.
［14］Xu Hongsheng, Zhang Ruiling. Application of ART and concept similarity in ecommerce recommendation systems ［J］. Journal of Computer Engineering and Applications, 2010,46(7):213217.(in Chinese)
附中文参考文献：
［4］李景.本体理论在文献检索系统中的应用研究［M］.北京：北京图书馆出版社,2005.
［9］梁健，王惠临.基于文本的本体学习方法研究［J］.情报理论与实践，2007，30(1):112115.
［10］杜小勇,李曼,王珊.本体学习研究综述［J］.软件学报, 2006,17(9)：18371847.
［11］徐红升，沈夏炯，刘宗田. 基于形式概念的语义网本体的构建与展现［J］.计算机科学, 2007,34(2)：171174.
［14］徐红升,张瑞玲. ART与概念相似在电子商务推荐系统中的应用［J］. 计算机工程与应用,2010,46(7):213217.

[1]	赵俊生, 王鑫宇, 尹玉洁, 张林. 基于蒙古语新闻领域本体的分布式检索方法[J]. 计算机工程与科学, 2021, 43(03): 560-570.
[2]	魏墨济, 赵燕清, 朱世伟, 李晨. 基于社会立场建模的网络犯罪预警研究[J]. 计算机工程与科学, 2021, 43(01): 151-160.
[3]	姜春茂, 刘安鹏. 三支决策视角下的属性约简加速方法[J]. 计算机工程与科学, 2020, 42(12): 2280-2286.
[4]	王亚强1,2，臧根林1,2，吴庆蓉1,2，占春丽1,2，谢新扬1,2. 一种领域知识图谱的本体根类型设计[J]. 计算机工程与科学, 2019, 41(10): 1861-1867.
[5]	佘志用,段超，张雷. 变精度最小平方粗糙熵的图像分割算法[J]. 计算机工程与科学, 2019, 41(04): 657-664.
[6]	吴尚智1，张文超2，佘志用1，张霞1，段超1. 利用蚁群优化算法的粗糙集属性约简方法[J]. 计算机工程与科学, 2019, 41(03): 538-544.
[7]	吴尚智1，佘志用2，张霞1，赵慧琴3. 利用变精度粗糙熵的图像分割算法[J]. 计算机工程与科学, 2018, 40(10): 1837-1843.
[8]	路文华，罗钧旻，李俊伟，高武奇. 基于自我意识的Agent通信语言研究[J]. 计算机工程与科学, 2018, 40(09): 1598-1605.
[9]	毛华,康然,杨兰珍. 基于固定β值的概念格上变精度粗糙集近似[J]. 计算机工程与科学, 2017, 39(12): 2345-2350.
[10]	谢小军1,2,俞春强3,王博1,何弦1,徐章艳1,2. 基于免疫量子粒子群优化的测试代价敏感属性约简算法[J]. 计算机工程与科学, 2017, 39(07): 1371-1378.
[11]	张思琪，邢薇薇,蔡圆媛. 一种基于WordNet的混合式语义相似度计算方法[J]. 计算机工程与科学, 2017, 39(05): 971-977.
[12]	盈艳,曹妍,牟向伟. 新闻社区领域本体构建研究[J]. 计算机工程与科学, 2016, 38(09): 1937-1942.
[13]	徐怡1,2，李策2. 基于多重阈值的变精度多粒度粗糙集模型[J]. 计算机工程与科学, 2016, 38(08): 1727-1734.
[14]	钟艳如，卢宏成，曾聪文. 装配公差综合领域本体知识库的构建[J]. 计算机工程与科学, 2016, 38(07): 1413-1418.
[15]	王红，孙康. 基于HBase的领域本体存储方法及其应用研究[J]. J4, 2016, 38(07): 1322-1329.