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New Technology of Library and Information Service  2011, Vol. 27 Issue (12): 46-51    DOI: 10.11925/infotech.1003-3513.2011.12.07
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Study on Ontology Hierarchy Relation Induction on Clustering Algorithm
Gu Jun1,2, Zhu Ziyang3
1. Department of Information Management, Nanjing University, Nanjing 210093, China;
2. Baoshan Iron and Steel Company Ltd., Shanghai 201900, China;
3. Library of Nanjing University of Information Science and Technology, Nanjing 210044, China
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Abstract  This paper proposes a method,which clusters the initial terms collection by ant colony algorithm and clusters the results hierarchy by K-means algorithm, then gets the labels of classes using the comprehensive similarity calculation, finishes the term hierarchy relation’s structure at last. Parts of experimental results are appraised and analyzed by domain experts.
Key wordsOntology      Semantic hierarchy      Ant colony algorithm      Clustering     
Received: 20 October 2011      Published: 02 February 2012



Cite this article:

Gu Jun, Zhu Ziyang. Study on Ontology Hierarchy Relation Induction on Clustering Algorithm. New Technology of Library and Information Service, 2011, 27(12): 46-51.

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[1] Berners-Lee T, Hendler J,Lassila O. The Semantic Web[J]. Scientific American, 2001 (5): 28-37.

[2] Ying D, Schubea F. Ontology Research and Development:Part I-A Review of Ontology Generation [J]. Journal of Information Science, 2002, 28(2):123-136.

[3] Harris Z S. Mathematical Structures of Language[M]. New York:Wiley, 1968.

[4] Carbalb S A. Automatic Construction of a Hypemym-labeled Noun Hierarchy from Text[C].In:Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, Maryland.1999:120-126.

[5] Fisher D H. Knowledge Acquisition via Incremental Conceptual Clustering[J]. Machine Learning,1987,2(2):139-172.

[6] Cimiano P, Staab S, Tane J. Automatic Acquisition of Taxonomies from Text FCA Meets NLP[C]. In:Proceedings of the International Workshop on Adaptive Text Extraction and Mining, Seattle,USA.2003:301-309.

[7] 马辉民, 李卫华, 吴良元. VSM在中文文本聚类中的应用及实证分析[J]. 武汉理工大学学报:信息与管理工程版,2006, 28(4): 56-59.

[8] 乐兵,王明文. 基于遗传算法的动态文本聚类[J]. 江西师范大学学报:自然科学版,2006, 30(3): 278-281.

[9] 龚静, 李安民. 一种改进的K-means中文文本聚类算法[J]. 湖南工业大学学报,2008,22(2): 52-54.

[10] 王刚,钟国祥. 一种基于本体相似度计算的文本聚类算法研究[J]. 计算机科学,2010, 37(9): 222-224.

[11] 温春,石昭祥,杨国正. 一种利用度属性获取本体概念层次的方法[J]. 小型微型计算机系统, 2010(2): 322-326.

[12] 季培培,鄢小燕,岑咏华,等. 面向领域中文文本信息处理的术语语义层次获取研究[J]. 现代图书情报技术,2010(9): 37-41.

[13] 余永红,柏文阳. 基于特征项权重自动分解的文本聚类[J]. 计算机工程,2011, 37(11): 25-27.

[14] 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.

[15] TF-IDF[EB/OL].[2011-11-13].

[16] Cosine Similarity[EB/OL].[2011-11-13].

[17] Dorigo M, Blum C. Ant Colony Optimization Theory:A Survey[J]. Theoretical Computer Science, 2005,344(2-3):243-278.

[18] Deneubourg J L, Goss S, Franks N, et al. The Dynamics of Collective Sorting Robot-like Ants and Ant-like Robots[C]. In:Proceedings of the International Conference on Simulation of Adaptive Behavior: From Animals to Animates.1991:356-363.

[19] 段海滨. 蚁群算法原理及其应用[M].北京:科学出版社, 2005.

[20] Lumer E, Faiea B. Diversity and Adaption in Populations of Clustering Ants[C]. In:Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior. Cambridge, MA: MIT Press,1994:501-508.

[21] Alan L P, Scott W C. Tech Mining[M]. New Jersey: John Wiley & Sons, Inc., 2005.
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