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Automatic Document Topic Identification using Wikipedia Hierarchical Ontology
Abstract The rapid growth in the number of documentThe rapid growth in the number of documents available to end users from around the world has led to a greatly-increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. In this work, a novel technique is proposed, to automatically construct a background knowledge structure in the form of a hierarchical ontology, using one of the largest online knowledge repositories: Wikipedia. Then, a novel approach is presented to automatically identify the documents' topics based on the proposed Wikipedia Hierarchical Ontology (WHO). Results show that the proposed model is efficient in identifying documents' topics, and promising, as it outperforms the accuracy of the other conventional algorithms for document clustering.tional algorithms for document clustering.
Abstractsub The rapid growth in the number of documentThe rapid growth in the number of documents available to end users from around the world has led to a greatly-increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. In this work, a novel technique is proposed, to automatically construct a background knowledge structure in the form of a hierarchical ontology, using one of the largest online knowledge repositories: Wikipedia. Then, a novel approach is presented to automatically identify the documents' topics based on the proposed Wikipedia Hierarchical Ontology (WHO). Results show that the proposed model is efficient in identifying documents' topics, and promising, as it outperforms the accuracy of the other conventional algorithms for document clustering.tional algorithms for document clustering.
Bibtextype inproceedings  +
Doi 10.1109/ISSPA.2012.6310552  +
Has author Hassan M.M. + , Fakhri Karray + , Kamel M.S. +
Has extra keyword Background knowledge + , Conventional algorithms + , Document Clustering + , End users + , Knowledge repository + , Machine understanding + , Novel techniques + , Rapid growth + , Text mining + , Topic identification + , Wikipedia + , Data mining + , Human-computer interaction + , Information science + , Signal processing + , Websites +
Isbn 9781467303828  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 237–242  +
Published in 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 +
Title Automatic Document Topic Identification using Wikipedia Hierarchical Ontology +
Type conference paper  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 02:39:50  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 02:39:50  +
DateThis property is a special property in this wiki. 2012  +
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