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This shortcoming prompted the authors to develop an automatic topic ontology construction process. However, in the past many attempts have been made by other researchers to utilize the automatic construction of ontology, which turned out to be challenging due to time, cost and maintenance. In this paper, the authors have proposed a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying the concepts and their associated semantic relationship with corpus based external knowledge resources, such as Wikipedia and WordNet. This topic ontology construction approach relies on concept acquisition and semantic relation extraction. A Jena API framework has been developed to organize the set of extracted semantic concepts, while Prot\u00e9g\u00e9 provides the platform to visualize the automatically constructed topic ontology. To evaluate the performance, web documents were classified using SVM classifier based on ODP and topic ontology. The topic ontology based classification produced better accuracy than ODP.<\/p>","DOI":"10.4018\/jiit.2013070104","type":"journal-article","created":{"date-parts":[[2013,11,12]],"date-time":"2013-11-12T20:19:19Z","timestamp":1384287559000},"page":"61-89","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia"],"prefix":"10.4018","volume":"9","author":[{"given":"V.","family":"Subramaniyaswamy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, SASTRA University, Thanjavur, Tamilnadu, India"}]}],"member":"2432","reference":[{"key":"jiit.2013070104-0","unstructured":"Agirre, E., Ansa, O., Martinez, D., & Hovy, E. (2001). Enriching WordNet concepts with topic signatures. 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