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.

Schlagwörter

Fachgebiet (DDC)

330 - Wirtschaft

Projekt

Veranstaltung

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

Startdatum der Ausstellung

Enddatum der Ausstellung

Startdatum der Konferenz

Enddatum der Konferenz

Datum der letzten Prüfung

ISBN

ISSN

Sprache

Englisch

Während FHNW Zugehörigkeit erstellt

Ja

Zukunftsfelder FHNW

Publikationsstatus

Veröffentlicht

Begutachtung

Peer-Review der ganzen Publikation

Open Access-Status

Diamond

Lizenz

Zitation

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

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