A hybrid intelligent approach combining machine learning and a knowledge graph to support academic journal publishers addressing the Reviewer Assignment Problem (RAP)

Abstract

This paper presents a hybrid intelligent approach that combines natural language processing (NLP) and knowledge engineering to address the Reviewer Assignment Problem (RAP) in scientific peer-review. The approach uses NLP techniques to match a new document with subject experts, and it employs a knowledge graph to identify conflicts of interest (COIs) between the authors of a document and potential reviewers. The approach detects three types of COIs: direct co-authorship, second-level coauthorship, and collaborators from the same institutions. Further, it uses semantic text similarity (STS) matching for peer-reviewing of documents in journals, where potential reviewers are screened from large literature databases. The research approach follows the Design Science Research methodology, where a prototypical system is designed based on the requirements elicited from both the literature and from primary data collection conducted in a publishing house. The approach is evaluated by implementing real-world use cases in the working prototype and by conducting a focus group with potential users, i.e., editors. © 2023 CEUR-WS. All rights reserved.