Learning Specific Ontology for Un-Supervised Text Classification

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The dramatic rise in massive text data has led to an increasing number of challenges in scalability and noisy information. Supervised classification has become expensive and time consuming as acquiring training sets for a large number of categories becomes more complex and classifiers are sensitive to data. Un-supervised classification has become an attractive alternative given it does not require training sets. However, un-supervised classification is still complex and there is a gap between understanding of concepts and features. This project aims to exploit domain ontology to find specific ontology which can bridge the gap, leading to a breakthrough for un-supervised classification. It provides foundations for classifying big text data.