An efficient ontology-based topic-specific article recommendation model for best-fit reviewers

Gohar Rehman Chughtai (), Jia Lee (), Mahnoor Shahzadi, Asif Kabir and Muhammad Arshad Shehzad Hassan
Additional contact information
Gohar Rehman Chughtai: Chongqing University
Jia Lee: Chongqing University
Mahnoor Shahzadi: University of Electronic Science and Technology
Asif Kabir: Kotli University
Muhammad Arshad Shehzad Hassan: Chongqing University

Scientometrics, 2020, vol. 122, issue 1, No 11, 249-265

Abstract: Abstract In general peer review is accredited as the vital and utmost cornerstone of the scientific publishing and research developments. Undeniably, the reviewers play a decisive role in ensuring the qualitative scientific developments published in any venue (Journals, conferences). The conventional time-tested method of double-blind peer review has been criticized having the flaws of inability to find the novelty, paucity of clarity, paucity of soundness, prone to be biased, the paucity of impartiality, discrepancies amongst reviewers, the paucity of acknowledgment and inspiration to reviewers. In order to cope with some of its flaws and to ensure the excellence of peer review, it is indispensable to delve into the process of article recommendation to the best fit reviewers. Typically, this recommendation is done by the human expert, so less accurate as the manual recommendation is incapable of initial scrutinizing of the tome of articles submitted and best fitting reviewer’s profile. This work proposes ontology and topic-specific personalized recommendation system to recommend the articles to the best-fit reviewers. In this proposed ontology-based model, latent semantic analysis and entropy have been deployed for similarity measure and topic-specificity indicator, thus to fetch the information of the best-fitted reviewer’s profile. In this work, an experimental arrangement has been set up relying on the primary dataset related to the reviewer’s profile and article reviewed. Results show the feasibility of the proposed model and the correlational relationship between the semantics and the topic-specificity of the articles which could be adopted as an automatic article recommendation to best fitting reviewers.

Keywords: Automatic personalized recommendation (APR); LSA; Entropy measure; Correlational analysis; Ontology; Bibliometric (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-019-03261-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:122:y:2020:i:1:d:10.1007_s11192-019-03261-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-019-03261-2

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().