Automatic construction of knowledge tree based on text clustering
Jie Liu · Jisuanji yingyong yanjiu · 2010
The construction and maintenance of the knowledge tree is an important and time-consuming task in a knowledge management system(KMS).This paper presented a novel method to construct the knowledge tree based on text clustering.Because it's difficult to extract concepts and vocabulary corresponding to nodes in knowledge tree while clustering by traditional K-means and SOM algorithms,selected PLSA(probabilistic latent semantic analysis) to construct knowledge tree.Experiment shows that the clustering accuracy of the new method is higher than the traditional K-means and SOM algorithms.In addition,because the probabilistic relationship between the vocabulary and the concept(subject) has been established,the concepts of node in knowledge tree could be easily extracted while clustering documents by the new method.