Using ontology semantics to improve text documents clustering

使用本体语义提高文本聚类(英文)

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Reuters20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,CLU-TO,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.