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

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.

Keywords

Subject (DDC)

330 - Wirtschaft

Project

Event

AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023)

Exhibition start date

Exhibition end date

Conference start date

Conference end date

Date of the last check

ISBN

ISSN

Language

English

Created during FHNW affiliation

Yes

Strategic action fields FHNW

Publication status

Published

Review

Peer review of the complete publication

Open access category

Diamond

License

Citation

Rordorf, D. H.-P., Käser, J., Crego Corot, A. E., & Laurenzi, E. (2023). A hybrid intelligent approach combining machine learning and a knowledge graph to support academic journal publishers addressing the Reviewer Assignment Problem (RAP). In A. Martin, H.-G. Fill, A. Gerber, K. Hinkelmann, D. Lenat, R. Stolle, & F. Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023) (pp. 1–19). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-11149

Full item page