Please wait a minute...
New Technology of Library and Information Service  2012, Vol. 28 Issue (2): 34-40    DOI: 10.11925/infotech.1003-3513.2012.02.06
Current Issue | Archive | Adv Search |
Development of Domain Ontology in Information Science Based on FCA and Association Rules
Liu Ping, Hu Yuehong
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
Download: PDF(840 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  This paper presents a new approach to Ontology learning in the domain of information science. A combination of Formal Concept Analysis (FCA) and association rules is used to facilitate Ontology construction from unstructured text. This approach acquires key concepts from documents by using a seeding and expansion mechanism; formulates (key concept by document) context for concept lattice construction, and bootstraps the learning of domain-specific concept hierarchies using FCA; extracts the relationships between the concepts via association rules. To evaluate the quality of the learned Ontology, a comparison with “Golden Standard” is undertaken, and the evaluation results illustrate that it can reach high domain coverage and identify some implicit relations between concepts. It is concluded that the proposed method is practical and useful to support the process of building domain Ontology.
Key wordsOntology development      Information science      FCA      Association rule     
Received: 08 September 2011      Published: 23 March 2012
: 

TP391

 

Cite this article:

Liu Ping, Hu Yuehong. Development of Domain Ontology in Information Science Based on FCA and Association Rules. New Technology of Library and Information Service, 2012, 28(2): 34-40.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2012.02.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2012/V28/I2/34

[1] 王崇德. 情报学简介[M]. 天津:天津大学出版社,1993:30-33.(Wang Chongde. Introduction to Information Science[M]. Tianjin: Tianjin University Press, 1993: 30-33.)

[2] Wille R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts[C]. In: the Ordered sets Dordrecht—Boston, Reidel, 1982: 445-470.

[3] 滕广青,毕强. 国外形式概念分析与概念格理论应用研究的前沿进展及热点分析[J]. 现代图书情报技术 ,2010(11):17-23.(Teng Guangqing, Bi Qiang. Analysis of the Progress and Hotspots in Applied Research of FCA and Concept Lattice Theory Abroad[J]. New Technology of Library and Information Service, 2010(11):17-23.)

[4] 张云中.基于形式概念分析的领域本体构建方法研究[D]. 长春:吉林大学, 2009.(Zhang Yunzhong. Research on Domain Ontology Construction Method Based on Formal Concept Analysis[D]. Changchun: Jilin University, 2009.)

[5] Cimiano P, Stumme G, Hotho A, et al. Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies[C]. In: Proceedings of the 2nd International Conference on Formal Concept Analysis (ICFCA). Springer, 2004:189-207.

[6] Gu T. Using Formal Concept Analysis for Ontology Structuring and Building[C]. In: Proceedings of the International Standard Industrial Classification(ISIC). Nanyang Technological University, 2003.

[7] Haav H M. A Semi-automatic Method to Ontology Design by Using FCA[C]. In: Proceedings of the 2nd International CLA Workshop, Concept Lattices and Their Applications. Technical University of Ostrava, 2004: 13-25.

[8] Obitko M, Snáel V, Smid J. Ontology Design with Formal Concept Analysis [C]. In: Proceedings of the CLA 2004 International Workshop on Concept Lattices and Their Applications. Technical University of Ostrava, 2004: 111-119.

[9] Han J, Kamber M. Data Mining: Concepts and Techniques [R/OL]. [2011-01-23]. http://134.208.3.165/course/2006/Fall/Data_mining/06.pdf.

[10] Maedche A, Staab S. Discovering Conceptual Relations from Text[C]. In: Proceedings of the European Conference on Artificial Intelligence (ECAI). Amsterdam:IOS Press, 2000: 321-325.

[11] Maedche A, Staab S. Ontology Learning for the Semantic Web[C]. In: Proceedings of the IEEE Intelligent Systems. 2001: 72-79.

[12] Noy N F, McGuinness D L. Ontology Development 101: A Guide to Creating Your First Ontology[R]. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, 2001:4-23.

[13] Uschold M, Gruninger M. Ontologies: Principles, Methods and Applications[J]. Knowledge Engneer Revision,1996,11 (2):93-155.

[14] 何琳. 领域本体的半自动构建及检索研究[M]. 南京:东南大学出版社, 2009:99-100.(He Lin. Research on Semi-automatic Construction and Retrieval of Domain Ontology[M]. Nanjing: Southeast University Press, 2009:99-100.)

[15] Ji D H, Zhao S J, Xiao G Z. Chinese Document Re-ranking Based on Automatically Acquired Term Resource[J]. Language Resource & Evaluation, 2009, 43(4):385-406.

[16] Yang Y M, Pederson J O. A Comparative Study on Feature Selection in Text Categorization[C]. In: Proceedings of the 14th International Conference on Machine Learning. Nashville:Morgan Kaufmann, 1997:412-420.

[17] 王俊华. 基于文本的半监督领域本体构建[D]. 长春: 吉林大学, 2010:19-45.(Wang Junhua. Semi-supervised Domain Ontology Building Based on Text[D]. Changchun: Jilin University, 2010:19-45.)

[18] Overview on ConExp[EB/OL]. [2011-06-28]. http: //conexp.sourceforge.net /users.

[19] IHMC CmapTools [EB/OL]. [2011-03-18]. http://cmap.ihmc.us/download/.

[20] 杜小勇,马文峰.领域本体评价研究[J]. 图书情报工作 ,2006,50(10):68-72.(Du Xiaoyong, Ma Wenfeng. An Evaluation Framework for Domain Ontology[J]. Library and Information Service, 2006,50(10):68-72.)

[21] 何琳.领域本体评价研究[J]. 图书馆杂志 ,2010,29(2):57-62.(He Lin. Research on Evaluation Mechanism of Domain Ontology[J]. Library Journal, 2010,29(2):57-62.)

[22] Bates M J. An Operational Definition of the Information Disciplines [EB/OL]. [2011-03-20].http://gseis.ucla.edu/faculty/bates/articles/pdf/Contribution512-1.pdf.

[23] Sabou M, Wroe C, Goble C, et al. Learning Domain Ontologies for Web Service Descriptions: An Experiment in Bioinformatics[C]. In: Proceedings of the 14th International World Wide Web Conference Committee (WWW2005). New York: ACM Press, 2005:190-198.
[1] Yong Zhang,Shuqing Li,Yongshang Cheng. Mining Algorithm for Weighted Association Rules Based on Frequency Effective Length[J]. 数据分析与知识发现, 2019, 3(7): 85-93.
[2] Yue He,Yue Feng,Shupeng Zhao,Yufeng Ma. Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics[J]. 数据分析与知识发现, 2018, 2(9): 42-49.
[3] 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.
[4] Yue He,Aixin Wang,Yue Feng,Li Wang. Optimizing Layouts of Outpatient Pharmacy Based on Association Rules[J]. 数据分析与知识发现, 2018, 2(1): 99-108.
[5] Xuanhui Zhang,Yuxiang Zhao. Evolution Path and Hot Topics of Citizen Science Studies[J]. 数据分析与知识发现, 2017, 1(7): 22-34.
[6] Xing Wei,Dehua Hu,Minhan Yi,Qizhen Zhu,Wenjie Zhu. Extracting Disease-Gene-Drug Correlations Based on Data Cube[J]. 数据分析与知识发现, 2017, 1(10): 94-104.
[7] Mingxuan Huang. Cross Language Information Retrieval Model Based on Matrix-weighted Association Patterns Mining[J]. 数据分析与知识发现, 2017, 1(1): 26-36.
[8] Guangce Ruan, Lei Xia. Mining Document Topics Based on Association Rules[J]. 数据分析与知识发现, 2016, 32(12): 50-56.
[9] Zhang Jinzhu,Wang Xiaomei,Han Tao. Predicting Co-authorship with Combination of Paths in Paper-author Bipartite Networks[J]. 现代图书情报技术, 2016, 32(10): 42-49.
[10] Du Siqi, Li Honglian, Lv Xueqiang. Research of Chinese Chunk Parsing in Application of the Product Feature Extraction[J]. 现代图书情报技术, 2015, 31(9): 26-30.
[11] Hao Mei, Wang Daoping. Mining Customer Focus Features from Product Reviews Oriented Supply Chain[J]. 现代图书情报技术, 2014, 30(4): 65-70.
[12] Qiu Junping, Yu Houqiang. The Research Development of Visual Analytics from the Perspective of VAST Conference[J]. 现代图书情报技术, 2014, 30(10): 14-24.
[13] Liu Wei, Zhu Zhongming, Zhang Wangqiang, Wang Sili, Yao Xiaona, Lu Linong. Implementation of Semantic Retrieval Based on Ontology Created by SKOS and Association Rule Mining[J]. 现代图书情报技术, 2013, 29(7/8): 22-27.
[14] Wang Yong, Zhang Qin, Yang Xiaojie. Research on the Method of Extracting Features from Chinese Product Reviews on the Internet[J]. 现代图书情报技术, 2013, (12): 70-73.
[15] Huang Mingxuan, Yu Ru. Information Retrieval System Based on Negative Association Rules and Frequent Itemsets Mining[J]. 现代图书情报技术, 2011, 27(7/8): 91-96.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn