Automatic Document Topic Identification using Wikipedia Hierarchical Ontology
|Automatic Document Topic Identification using Wikipedia Hierarchical Ontology|
|Author(s)||Hassan M.M., Karray F., Kamel M.S.|
|Published in||2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012|
|Keyword(s)||Unknown (Extra: 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)|
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Automatic Document Topic Identification using Wikipedia Hierarchical Ontology is a 2012 conference paper written in English by Hassan M.M., Karray F., Kamel M.S. and published in 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012.
The 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.
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