Document Type
Article
Publication Date
2025
First page number:
1
Last page number:
13
Abstract
In this paper, a relational database schema is introduced that supports rapid prototyping, data preprocessing, and warehousing tasks associated with research profiling studies, natural language processing, and bibliometric analysis. Python scripts are leveraged for the seamless retrieval and processing of data from Semantic Scholar. This schema is tailored to efficiently analyze entities such as authors, their scientific papers, referenced papers, and cited papers. Adhering to the relational model, this schema offers a standardized approach to data storage and detailed information retrieval for scientific papers. Enhancing knowledge discovery in scientific databases, this schema provides researchers with a powerful platform for robust data management, retrieval, and analysis.
Keywords
Database design; Relational databases; Data interchange; Semantic scholar API
Disciplines
Cataloging and Metadata | Library and Information Science | Programming Languages and Compilers
File Format
File Size
576 KB
Language
English
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Melvin, D.
(2025).
Relational Database Schema to Support Research Profiling Studies, Natural Language Processing, and Bibliometric Analysis.
1-13.
https://digitalscholarship.unlv.edu/lib_articles/765
