Soumya George, M. Sudheep Elayidom, T. Santhanakrishnan

Subject Classification of scholarly articles is a pertinent area in the field of research. Proper classification of journal articles is an essential criterion for academic search engines to facilitate easier search and retrieval of journal papers based on user preferred research areas. Subject classification is equally important for search engines to find appropriate reviewers to review submitted papers based on area. It also helps to implement an efficient paper recommendation system to recommend similar articles to users based on their areas of interest. The widely used approach for subject classification is to use metadata of journal papers like title, abstract, paper keywords etc. to classify articles or by insisting users to use some classification system to specify the subject area of their article. This paper proposes an efficient graph based subject classification of journal articles using a pre-indexed classifier model by means of full text indexing approach. Journal contents are indexed using Sequence Word Graph model to classify any journal article into its relevant research areas and sub areas based on actual keyword or key phrase embedding in the journal contents. This automatic classification approach enables efficient search of scholarly articles by means of subject categories or by sub areas. The subject classification accuracy is tested using arXiv subject classified papers set of total 1307 papers and accuracy yields 91%.

Volume 11 | 02-Special Issue

Pages: 1763-1776