Automation Applied to the Collection and Generation of Scientific Literature

Nadia Paola Valadez- de la Paz, Jose Antonio Vazquez-Lopez, Aidee Hernandez-Lopez, Jaime Francisco Aviles-Viñas, Jose Luis Navarro-Gonzalez, Alfredo Valentin Reyes-Acosta and Ismael Lopez-Juarez ()
Additional contact information
Nadia Paola Valadez- de la Paz: Departamento de Ingenieria Industrial, Tecnologico Nacional de Mexico, Instituto Tecnologico de Celaya, Celaya 38010, Mexico
Jose Antonio Vazquez-Lopez: Departamento de Ingenieria Industrial, Tecnologico Nacional de Mexico, Instituto Tecnologico de Celaya, Celaya 38010, Mexico
Aidee Hernandez-Lopez: Departamento de Ingenieria Industrial, Universidad del SABES, Campus Celaya, Leon 37234, Mexico
Jaime Francisco Aviles-Viñas: Mechatronics Group, Engineering Faculty, Autonomous University of Yucatan, Merida 97302, Mexico
Jose Luis Navarro-Gonzalez: Departamento de Ingenieria Mecanica, Tecnologico Nacional de Mexico, Instituto Tecnologico de Saltillo, Saltillo 25280, Mexico
Alfredo Valentin Reyes-Acosta: Facultad de Sistemas, Unidad Saltillo, Universidad Autonoma de Coahuila, Saltillo 25350, Mexico
Ismael Lopez-Juarez: Mechatronics Group, Engineering Faculty, Autonomous University of Yucatan, Merida 97302, Mexico

Publications, 2025, vol. 13, issue 1, 1-24

Abstract: Preliminary activities of searching and selecting relevant articles are crucial in scientific research to determine the state of the art (SOTA) and enhance overall outcomes. While there are automatic tools for keyword extraction, these algorithms are often computationally expensive, storage-intensive, and reliant on institutional subscriptions for metadata retrieval. Most importantly, they still require manual selection of literature. This paper introduces a framework that automates keyword searching in article abstracts to help select relevant literature for the SOTA by identifying key terms matching that we, hereafter, call source words . A case study in the food and beverage industry is provided to demonstrate the algorithm’s application. In the study, five relevant knowledge areas were defined to guide literature selection. The database from scientific repositories was categorized using six classification rules based on impact factor (IF), Open Access (OA) status, and JCR journal ranking. This classification revealed the knowledge area with the highest presence and highlighted the effectiveness of the selection rules in identifying articles for the SOTA. The approach included a panel of experts who confirmed the algorithm’s effectiveness in identifying source words in high-quality articles. The algorithm’s performance was evaluated using the F 1 Score, which reached 0.83 after filtering out non-relevant articles. This result validates the algorithm’s ability to extract significant source words and demonstrates its usefulness in building the SOTA by focusing on the most scientifically impactful articles.

Keywords: state-of-the-art; machine learning; knowledge management; keyword extraction; systematic literature review (search for similar items in EconPapers)
JEL-codes: A2 D83 L82 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2304-6775/13/1/11/pdf (application/pdf)
https://www.mdpi.com/2304-6775/13/1/11/ (text/html)

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:gam:jpubli:v:13:y:2025:i:1:p:11-:d:1606744

Access Statistics for this article

Publications is currently edited by Ms. Jennifer Zhang

More articles in Publications from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().