|Table of Contents|

[1] Luo Na, Zuo Wanli, Yuan Fuyu, et al. Using ontology semantics to improve text documents clustering [J]. Journal of Southeast University (English Edition), 2006, 22 (3): 370-374. [doi:10.3969/j.issn.1003-7985.2006.03.017]
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Using ontology semantics to improve text documents clustering()
使用本体语义提高文本聚类
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
22
Issue:
2006 3
Page:
370-374
Research Field:
Automation
Publishing date:
2006-09-30

Info

Title:
Using ontology semantics to improve text documents clustering
使用本体语义提高文本聚类
Author(s):
Luo Na1;  2;  Zuo Wanli1;  Yuan Fuyu1;  Zhang Jingbo2;  Zhang Huijie2
1 College of Computer Science and Technology, Jilin University, Changchun 130012, China
2 School of Computer Science, Northeast Normal University, Changchun 130024, China
罗娜1;  2;  左万利1;  袁福宇1;  张靖波2;  张慧杰2
1吉林大学计算机科学与技术学院, 长春 130012; 2东北师范大学计算机学院, 长春 130024
Keywords:
ontology;  text clustering;  lexicon;  WordNet
本体;  文本聚类;  词典;  WordNet
PACS:
TP181
DOI:
10.3969/j.issn.1003-7985.2006.03.017
Abstract:
In order to improve the clustering results and select in the results, the ontology semantic is combined with document clustering.A new document clustering algorithm based WordNet in the phrase of document processing is proposed.First, every word vector by new entities is extended after the documents are represented by tf-idf.Then the feature extracting algorithm is applied for the documents.Finally, the algorithm of ontology aggregation clustering(OAC)is proposed to improve the result of document clustering.Experiments are based on the data set of Reuters 20 News Group, and experimental results are compared with the results obtained by mutual information(MI).The conclusion draws that the proposed algorithm of document clustering based on ontology is better than the other existed clustering algorithms such as MNB, CLUTO, co-clustering, etc.
为了提高聚类结果和允许在结果中进行选择, 将本体语义与文档聚类相结合, 在文档处理过程中提出了基于WordNet的新的文档聚类算法.首先通过tf-idf对文档进行了表示, 为了将WordNet的概念出现在文档集合中, 通过新的实体对每一个单词向量进行扩展.其次, 运用特征提取算法对文档进行特征提取.最后提出了本体集合聚类算法用以提高文本的聚类效果.实验构建在Reuters 20新闻组的数据基础上, 应用互信息作为试验结果的比较.结果表明:与已经存在的一些算法如MNB, CLUTO, co-clustering等相比, 基于本体的聚类算法在文本聚类上有很明显的提高.

References:

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Memo

Memo:
Biographies: Luo Na(1980—), female, graduate;Zuo Wanli(corresponding author), male, doctor, professor, wanli@jlu.edu.cn.
Last Update: 2006-09-20