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Automatic topic ontology construction using semantic relations from wordnet and wikipedia
Abstract Due to the explosive growth of web technolDue to the explosive growth of web technology, a huge amount of information is available as web resources over the Internet. Therefore, in order to access the relevant content from the web resources effectively, considerable attention is paid on the semantic web for efficient knowledge sharing and interoperability. Topic ontology is a hierarchy of a set of topics that are interconnected using semantic relations, which is being increasingly used in the web mining techniques. Reviews of the past research reveal that semiautomatic ontology is not capable of handling high usage. 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 Protégé 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. Copyrightoduced better accuracy than ODP. Copyright
Abstractsub Due to the explosive growth of web technolDue to the explosive growth of web technology, a huge amount of information is available as web resources over the Internet. Therefore, in order to access the relevant content from the web resources effectively, considerable attention is paid on the semantic web for efficient knowledge sharing and interoperability. Topic ontology is a hierarchy of a set of topics that are interconnected using semantic relations, which is being increasingly used in the web mining techniques. Reviews of the past research reveal that semiautomatic ontology is not capable of handling high usage. 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 Protégé 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. Copyrightoduced better accuracy than ODP. Copyright
Bibtextype misc  +
Doi 10.4018/jiit.2013070104  +
Has author Subramaniyaswamy V. +
Has extra keyword Automatic construction + , Ontology construction + , Open directory projects + , Semantic relation extractions + , Semantic relationships + , Web ontology language + , Wikipedia + , Wordnet + , Data mining + , Semantic web + , World Wide Web + , Ontology +
Has keyword Open directory project (ODP) + , Semantic web + , Topic ontology + , Web ontology language (OWL) + , Wikipedia + , Wordnet +
Issn 15483657  +
Issue 3  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 61–89  +
Published in International Journal of Intelligent Information Technologies +
Title Automatic topic ontology construction using semantic relations from wordnet and wikipedia +
Type Short Survey  +
Volume 9  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 6 November 2014 18:41:23  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 18:41:23  +
DateThis property is a special property in this wiki. 2013  +
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