Abstract
Today, one of the key tasks for Web based learning is to retrieve research articles of specific domains. To accomplish this task, data mining techniques and semantic web technologies can be used to retrieve user relevant documents. In this work we extract author supplied keywords from a collection of computer science articles, which has a strong influence on the topic of the article when compared with other words. For these keywords term weight is computed using Fuzzy Logic which uses three criteria namely map** with concept in domain ontology, keyword frequency in the title and keyword frequency in abstract. Using domain ontology, keywords of each document with their term weights are represented hierarchically as XML documents and they are clustered. The experimental results show that the proposed technique yields better precision and recall rates when compared with some of the existing approaches.
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Periakaruppan, R., Nadarajan, R. (2015). Automatic Clustering of Research Articles Using Domain Ontology and Fuzzy Logic. In: Chiu, D., et al. Advances in Web-Based Learning – ICWL 2013 Workshops. ICWL 2013. Lecture Notes in Computer Science(), vol 8390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46315-4_5
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DOI: https://doi.org/10.1007/978-3-662-46315-4_5
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