Please wait a minute...
New Technology of Library and Information Service  2013, Vol. 29 Issue (7/8): 43-48    DOI: 10.11925/infotech.1003-3513.2013.07-08.06
article Current Issue | Archive | Adv Search |
Study on Instance Learning Method of Internet User Preference Ontology
Zhu Hengmin1, Jia Danhua2, Huang Zhenqi3, Wang Chunhui1
1. Institute of ICT Development & Strategy, Nanjing University of Posts & Telecommunications, Nanjing 210023, China;
2. College of Economics & Management, Nanjing University of Posts & Telecommunications, Nanjing 210023, China;
3. Fujian Fujitsu Communication Software Co., Ltd., Fuzhou 350013, China
Download: PDF(649 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  Internet user preference Ontology can fully and accurately describe the interest and multidimensional preference of Internet users. In order to effectively resolve the problem that a large number of instances which are expanding and varying are hard to collect manually, the learning method of three representative instances including the topic professional website, brand and sporting events is researched. This method can achieve semi-automatic construction of Internet user preference Ontology. The experiments are designed to verify the effectiveness of the method.
Key wordsInternet      User preference      Ontology      Instance learning     
Received: 19 April 2013      Published: 02 September 2013
: 

G350.7

 

Cite this article:

Zhu Hengmin, Jia Danhua, Huang Zhenqi, Wang Chunhui. Study on Instance Learning Method of Internet User Preference Ontology. New Technology of Library and Information Service, 2013, 29(7/8): 43-48.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2013.07-08.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2013/V29/I7/8/43

[1] 方卫东,袁华,刘卫红.基于Web挖掘的领域本体自动学习[J]. 清华大学学报:自然科学版,2005,45(S1):1729-1733.(Fang Weidong, Yuan Hua, Liu Weihong. Automatic Domain Ontology Learning Based on Web Mining[J]. Journal of Tsinghua University:Science & Technology, 2005, 45(S1): 1729-1733.)
[2] Shamsfard M, Barforoush A A. Learning Ontologies from Natural Language Texts[J]. International Journal of Human-Computer Studies,2004,60(1):17-63.
[3] Turney P. Learning to Extract Key Phrases from Text[R].National Research Council of Canada,1999.
[4] 姜韶华,党延忠.基于长度递减与串频统计的文本切分算法[J]. 情报学报,2006,25(1):74-79.(Jiang Shaohua, Dang Yanzhong. Algorithm for Chinese Text Based on Length Descending and String Frequency Statistics[J]. Journal of the China Society for Scientific and Technical Information, 2006, 25(1):74-79.)
[5] Navigli R, Velardi P, Gangemi A. Ontology Learning and Its Application to Automated Terminology Translation[J].IEEE Intelligent Systems,2003,18(1):22-31.
[6] 岑咏华,韩哲,季培培.基于隐马尔科夫模型的中文术语识别研究[J]. 现代图书情报技术,2008(12):54-58.(Cen Yonghua, Han Zhe, Ji Peipei. Chinese Term Recognition Based on Hidden Markov Model[J]. New Technology of Library and Information Service, 2008(12):54-58.)
[7] 刘豹,张桂平,蔡东风.基于统计和规则相结合的科技术语自动抽取研究[J]. 计算机工程与应用,2008,44(23):147-150.(Liu Bao, Zhang Guiping, Cai Dongfeng. Technical Term Automatic Extraction Research Based on Statistics and Rules[J]. Computer Engineering and Applications, 2008, 44(23):147-150.)
[8] 谷俊,王昊.基于领域中文文本的术语抽取方法研究[J]. 现代图书情报技术,2011(4):29-34.(Gu Jun, Wang Hao. Study on Term Extraction on the Basis of Chinese Domain Texts[J].New Technology of Library and Information Service,2011(4):29-34.)
[9] 柳佳刚,龙军,李泽军.一种用于Web信息抽取的页面信息本体自动学习方法[J]. 计算技术与自动化,2011,30(1):119-123.(Liu Jiagang, Long Jun, Li Zejun. An Automatic Ontology Learning Approach Based on Web Information Items for Web Information Extraction[J].Computing Technology and Automation,2011,30(1):119-123.)
[10] 连乐新.基于本体的实例信息抽取与匹配技术研究[D].南京:南京大学,2007.(Lian Lexin. Study on Techniques of Case Information Extraction and Matching Based on Ontology[D]. Nanjing: Nanjing University, 2007.)
[11] 夏亚梅,苏森.面向本体实例生成的有限汉语语法学习系统[J]. 北京邮电大学学报,2010,33(5):37-40.(Xia Yamei, Su Sen. A Limited Chinese Grammar Acquisition System for the Generation of Ontology Instance[J]. Journal of Beijing University of Posts and Telecommunications,2010,33(5):37-40.)
[12] 朱恒民,黄震奇,贾丹华,等.挖掘电信客户多维偏好的标签本体研究[J]. 通信企业管理,2013(1):84-85.(Zhu Hengmin, Huang Zhenqi, Jia Danhua, et al. Study on Tag Ontology for Mining Telecom Customer Multidimensional Preference[J]. C-Enterprise Management, 2013 (1):84-85.)
[1] Shiqi Deng,Liang Hong. Constructing Domain Ontology for Intelligent Applications: Case Study of Anti Tele-Fraud[J]. 数据分析与知识发现, 2019, 3(7): 73-84.
[2] Zhu Fu,Yuefen Wang,Xuhui Ding. Semantic Representation of Design Process Knowledge Reuse[J]. 数据分析与知识发现, 2019, 3(6): 21-29.
[3] Guangshang Gao. A Survey of User Profiles Methods[J]. 数据分析与知识发现, 2019, 3(3): 25-35.
[4] Yanshuang Mei,Hengmin Zhu,Jing Wei. A Study on the Mechanism of Media Collaboration on the Spread of Internet Public Opinion[J]. 数据分析与知识发现, 2019, 3(2): 65-71.
[5] Ying Wang,Li Qian,Jing Xie,Zhijun Chang,Beibei Kong. Building Knowledge Graph with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 15-26.
[6] Youshi He,Shufang He. Sentiment Mining of Online Product Reviews Based on Domain Ontology[J]. 数据分析与知识发现, 2018, 2(8): 60-68.
[7] Longjia Jia,Bangzuo Zhang. Classifying Topics of Internet Public Opinion from College Students: Case Study of Sina Weibo[J]. 数据分析与知识发现, 2018, 2(7): 55-62.
[8] Huihui Tang,Hao Wang,Zixuan Zhang,Xueying Wang. Extracting Names of Historical Events Based on Chinese Character Tags[J]. 数据分析与知识发现, 2018, 2(7): 89-100.
[9] Beibei Pang,Juanqiong Gou,Wenxin Mu. Extracting Topics and Their Relationship from College Student Mentoring[J]. 数据分析与知识发现, 2018, 2(6): 92-101.
[10] Shengchun Ding,Menglu Liu,Zhu Fu. Unified Multidimensional Model Based on Knowledge Flow in Conceptual Design[J]. 数据分析与知识发现, 2018, 2(2): 11-19.
[11] Jingqi Wang,Rui Li,Huayi Wu. The Evolution of Online Public Opinion Based on Spatial Autocorrelation[J]. 数据分析与知识发现, 2018, 2(2): 64-73.
[12] Haili Tu,Xiaobo Tang. Building Product Recommendation Model Based on Tags[J]. 数据分析与知识发现, 2017, 1(9): 28-39.
[13] Yinxiu Hou,Weiqing Li,Weijun Wang,Tingting Zhang. Personalized Book Recommendation Based on User Preferences and Commodity Features[J]. 数据分析与知识发现, 2017, 1(8): 9-17.
[14] Erjing Chen,Enbo Jiang. Review of Studies on Text Similarity Measures[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
[15] Rujiang Bai,Fuhai Leng,Junhua Liao. An Improved Cosine Text Similarity Computing Method Based on Semantic Chunk Feature[J]. 数据分析与知识发现, 2017, 1(6): 56-64.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn