Welcome to the InfoSci Platform
Reference Hub6
Applying Semantic Relations for Automatic Topic Ontology Construction

Applying Semantic Relations for Automatic Topic Ontology Construction

Subramaniyaswamy Vairavasundaram, Logesh R.
Copyright: © 2018 |Pages: 30
ISBN13: 9781522536864|ISBN10: 1522536868|EISBN13: 9781522536871
DOI: 10.4018/978-1-5225-3686-4.ch004
Cite Chapter Cite Chapter

MLA

Vairavasundaram, Subramaniyaswamy, and Logesh R. "Applying Semantic Relations for Automatic Topic Ontology Construction." Developments and Trends in Intelligent Technologies and Smart Systems, edited by Vijayan Sugumaran, IGI Global, 2018, pp. 48-77. https://doi.org/10.4018/978-1-5225-3686-4.ch004

APA

Vairavasundaram, S. & R., L. (2018). Applying Semantic Relations for Automatic Topic Ontology Construction. In V. Sugumaran (Ed.), Developments and Trends in Intelligent Technologies and Smart Systems (pp. 48-77). IGI Global. https://doi.org/10.4018/978-1-5225-3686-4.ch004

Chicago

Vairavasundaram, Subramaniyaswamy, and Logesh R. "Applying Semantic Relations for Automatic Topic Ontology Construction." In Developments and Trends in Intelligent Technologies and Smart Systems, edited by Vijayan Sugumaran, 48-77. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3686-4.ch004

Export Reference

Mendeley
Favorite

Abstract

The rapid growth of web technologies had created a huge amount of information that is available as web resources on Internet. Authors develop an automatic topic ontology construction process for better topic classification and present a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying concepts and their associated semantic relationships based on external knowledge from Wikipedia and WordNet. The topic ontology construction process relies on concept acquisition and semantic relation extraction. Initially, a topic mapping algorithm is developed to acquire the concepts from Wikipedia based on semantic relations. A semantic similarity clustering algorithm is used to compute similarity to group the set of similar concepts. The semantic relation extraction algorithm derives associated semantic relations between the set of extracted topics from the lexical patterns in WordNet. The performance of the proposed topic ontology is evaluated for the classification of web documents and obtained results depict the improved performance over ODP.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.