Classification of text documents supervised by domain ontologies

Abstract

The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.

A Semantic based Terms Relation Research for Classification in Web Information Mining

“…The problem of automatic classification of information and WIS has been widely discussed in the past decade [8], [10], [29], [30]. Demand for supporting tools and methods is increasing as more and more information and services are constantly available in e-libraries, e-commerce, blogs and forums [4], [14] [15], [31] on the other hand, Clear texts regarding the subject matter area are also a very important subject in information processing techniques to discover knowledge. This task can be facilitated by providing means to oversee the classification of the set of texts examined to match domain services [5], [13], [32].…”