Subject Classification of Academic Journals via Knowledge Graph Embedding

Tracking #: 1937-3150

This paper is currently under review
Authors: 
Soumya George
M. Sudheep Elayidom
T. Santhanakrishnan

Responsible editor: 
Guest Editors Knowledge Graphs 2018

Submission type: 
Full Paper
Abstract: 
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. The widely used approach is to use metadata of journal papers like title, abstract, paper keywords etc. to classify articles. 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 and also relieves the journals to ask users to classify their papers to find proper reviewers. An attempt to find authors main streams or research areas based on the subject classification of their papers is also done. The subject classification accuracy is tested using arXiv subject classified papers set of total 1307 papers and accuracy yields 91%. Classification accuracy of author’s research areas of interest also tested manually for 52 authors having 2 or more papers indexed in the database by comparing with that of Google Scholar profile and got 100% accuracy for 28 authors. And out of the remaining 24 authors, 14 authors have only one field missing and rest of the authors have only 2 or 3 fields missing. A comparison of full text based subject classification with metadata based classification is also done and the results proved that full text indexing based subject classification yields high accuracy than metadata based classification.
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