Automated Ontology Building for News Text using Associative Word Properties
S.M.F.D Syed Mustapha1, Abdulmajeed Alsufyani2
1S.M.F.D Syed Mustapha*, Computer Science Department, College of Computing and Information Technology, Taif University, Makkah, Saudi Arabia.
2Abdulmajeed Alsufyani, Computer Science Department, College of Computing and Information Technology, Taif University, Makkah, Saudi Arabia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 663-668 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2641039520/2020©BEIESP | DOI: 10.35940/ijitee.F3340.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: It is known in the literature that ontology had been used extensively in machine learning for performance enhancement for text retrieval. It is also shown that robust ontology with detailed description of the domain knowledge will contribute to the accuracy in the retrieval. Nevertheless, we argue in some domain such as news text retrieval, building an ontology manually can be costly for a large-scale news repository and especially with the changes in content due to the dynamic events. In addition, maintenance can be a dauting task to keep up with new words that are associated with new events. This paper demonstrates the attempt to fully automate the development of an ontology for identifying the news domain and its subdomain. The ontology specification is defined based on the needs of the accuracy in retrieval. The mechanism of generating the ontology specification is defined and the results of the retrieval performance is discussed.
Keywords: News Text Retrieval, Ontology, Information Extraction and Text Mining.
Scope of the Article: Building Energy