Text Clustering Based on Domain Ontology and Latent Semantic Analysis

One key step in text mining is the categorization of texts, i.e., to put texts of the same or similar contents into one group so as to distinguish texts of different contents. However, traditional word-frequency-based statistical approaches, such as VSM model, failed to reflect the complicated meaning in texts. This paper ushers in domain ontology and constructs new conceptual vector space model in the pre-processing stage of text clustering, substituting the initial matrix (lexicon-text matrix) in the latent semantic analysis with concept-text matrix. In the clustering analysis stage, this model adopts semantic similarity, partially overcoming the difficulty in accurately and effectively evaluating the degree of similarity of text due to simply taking into account the frequency of words and/or phrases in the text. Experimental results indicate that this method is helpful in improving the result of text clustering.