Please wait a minute...
New Technology of Library and Information Service  2005, Vol. 21 Issue (11): 10-13    DOI: 10.11925/infotech.1003-3513.2005.11.02
article Current Issue | Archive | Adv Search |
Design and Simulative Implementation of Ontologybased
Wang Yuefen1   Wu Xiaolei2   Zhu Hailing1
1(Department of Information Management, Institute of Economic and Management,
Nanjing University of Science and Technology,Nanjing 210094,China)
2(China Shipbuilding Industry Corporation 714 Research Institute, Beijing 100085,China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

Aimming at the limitations of content analysis method under the network environment, the article puts forward a new research thought of content analysis method integrated with Ontology. It discusses basic thinking and feasibility of project. Then paper probes into the whole architecture and function and operation process of Ontology-based content analysis system, and introduces key technique and running condition of system implementation. Finally, a simulative system for Ontologybased content analysis is built associating with the instances.
Key words： Content analysis      Ontology      System design      Simulative experiment     
Received: 04 August 2005      Published: 25 November 2005
: 

G350

 		 
Corresponding Authors: Wang Yuefen     E-mail: yuefen163@163.com
About author:: Wang Yuefen,Wu Xiaolei,Zhu Hailing
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors

Cite this article:

Wang Yuefen,Wu Xiaolei,Zhu Hailing. Design and Simulative Implementation of Ontologybased. New Technology of Library and Information Service, 2005, 21(11): 10-13.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2005.11.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2005/V21/I11/10

1卜卫.试论内容分析方法.国际新闻界，1997（4）：55-59，68
2 刘春茂等.知识组织与知识管理的综合研究.情报学进展（第五卷），北京：国防工业出版社，2003
3 WP4: Ontology Infrastructure, KnowledgeAssisted Content Analysis, Semantic Reasoning and Intelligent Content Retrieval for the first 18 months.http://www.acemedia.org/aceMedia/project/work_breakdown/wp4.html(Accessed Jun.15,2005)
4 Nathalie Aussenac-Gilles.Supervised text analysis for ontology and terminology engineering.http://www.smi.ucd.ie/Dagstuhl-MLSW/ proceedings/aussenac-gilles.pdf(Accessed Jun.12,2005)
5 Integrating knowledge, semantics and content for usercentred intelligent media services. http://imsc.usc.edu/research/project/i4/i4_nsf.pdf(Accessed Jun.10,2005)
6 Peter Mika,Hans Akkermans.Towards a New Synthesis of Ontology Technology and Knowledge Management.Technical Report IR-BI-001,March, 2004
7 常春. Ontology在农业信息管理中的构建与转化.中国农业科学院科技文献信息中心博士研究生学位论文, 2004
[1] Sheng Shu, Huang Qi, Yang Yang, Xie Qiwen, Qin Xinguo. Exchanging Chinese Medical Information Based on HL7 FHIR[J]. 数据分析与知识发现, 2021, 5(11): 13-28.
[2] Zeng Zhen,Li Gang,Mao Jin,Chen Jinghao. Data Governance and Domain Ontology of Regional Public Security[J]. 数据分析与知识发现, 2020, 4(9): 41-55.
[3] Shaohua Qiang,Yunlu Luo,Yupeng Li,Peng Wu. Ontology Reasoning for Financial Affairs with RBR and CBR[J]. 数据分析与知识发现, 2019, 3(8): 94-104.
[4] 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.
[5] 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.
[6] Jing Shi,Chenlu Li,Yuxing Qian,Liqin Zhou,Bin Zhang. Information Needs of Domestic and International HCQA Users ——An Empirical Analysis[J]. 数据分析与知识发现, 2019, 3(5): 1-10.
[7] Guangshang Gao. A Survey of User Profiles Methods[J]. 数据分析与知识发现, 2019, 3(3): 25-35.
[8] 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.
[9] He Youshi,He Shufang. 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.
[10] Tang Huihui,Wang Hao,Zhang Zixuan,Wang Xueying. 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.
[11] Pang Beibei,Gou Juanqiong,Mu Wenxin. Extracting Topics and Their Relationship from College Student Mentoring[J]. 数据分析与知识发现, 2018, 2(6): 92-101.
[12] Ding Shengchun,Liu Menglu,Fu Zhu. 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.
[13] Tu Haili,Tang Xiaobo. Building Product Recommendation Model Based on Tags[J]. 数据分析与知识发现, 2017, 1(9): 28-39.
[14] Chen Erjing,Jiang Enbo. Review of Studies on Text Similarity Measures[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
[15] Bai Rujiang,Leng Fuhai,Liao Junhua. 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