A Graph-Based Tag Recommendation for Just Abstracted Scientific Articles Tagging
Djalila Boughareb, Abdennour Khobizi, Rima Boughareb, Nadir Farah, Hamid Séridi · International Journal of Cooperative Information Systems · 2020
Tags, when properly assigned to limited access papers, help users to estimate their relevance. This paper introduces a new approach for the selection of relevant tags as well as a recommendation for scientific papers tagging. The approach defines the relatedness between the tags attributed by users and the concepts extracted from the available sections of scientific papers based on statistical, structural and semantic aspects. Two different term-based graphs ([Formula: see text]-graph and [Formula: see text]-graph) were generated whose vertices indicate the terms and the edges represent the relatedness score between these terms. In addition, two algorithms were implemented to select and recommend the relevant tags: the neighbor-algorithm and the best-path-algorithm. The results of the experiments performed on a CiteULike collection of tagged papers show significant improvements only for the tagging of abstracted scientific articles. The approach was evaluated by referring to the full text of the papers with expert evaluation and comparing the tags generated by CiteULike users. Using the neighbor-algorithm, 80% of the top 10 recommended tags based on [Formula: see text]-graph and 76% of the top 10 recommended tags based on the [Formula: see text]-graph were relevant. While only 62% of those recommended by CiteULike users were relevant. The best-path-algorithm gave the best results in the top 20 and top 30 recommended tags and this in comparison with the tags recommended by the neighbor-algorithm and the tags assigned by CiteULike users.