Abstract
Due to the constant increase in the publication of scientific articles in databases worldwide, searching for relevant academic documents for research has become a complex process that sometimes requires significant time and effort. This research article addresses this problem by developing an interactive web system that makes it easier for users to identify the similarity of scientific articles for their research. The system allows users to upload a dataset of scientific articles with three attributes, titles, keywords, and abstracts, to analyze their similarities. The methodology uses NLP techniques for pre-processing and homogenization of the attributes. Then, we calculated similarity matrices using two metrics that assemble the three attributes into a single weighted matrix to measure the divergence between scientific documents. The results are presented through a web interface, including summary tables, heat maps, two-dimensional scatter diagrams, and clustering graphs. The experiment was carried out using a dataset of 192 scientific articles from different academic databases covering the areas of formal sciences, computer sciences, medicine, and social sciences. The results revealed more significant similarities between the documents in the same study field.
This work was supported by the IDEIAGEOCA Research Group of the Universidad Politécnica Salesiana in Quito, Ecuador.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alchokr, R., Borkar, M., Thotadarya, S., Saake, G., Leich, T.: Supporting systematic literature reviews using deep-learning-based language models. In: Proceedings of the 1st International Workshop on Natural Language-based Software Engineering, pp. 67–74 (2022)
Almas, J., Qamar, U.: Affect of data filter on performance of latent semantic analysis based research paper recommender system. In: 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), pp. 50–54. IEEE (2020)
Cai, X., Xiao, B., Wang, C., Zhang, R.: Quadratic-chi similarity metric learning for histogram feature. In: The First Asian Conference on Pattern Recognition, pp. 47–51. IEEE (2011)
Cardoso Ermel, A.P., Lacerda, D.P., Morandi, M.I.W.M., Gauss, L.: Systematic literature review. In: Ermel, A.P.C., Lacerda, D.P., Morandi, M.I.W.M., Gauss, L. (eds.) Literature Reviews, pp. 19–30. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75722-9_3
Chen, J., Liu, Y., Zhao, S., Zhang, Y.: Citation recommendation based on weighted heterogeneous information network containing semantic linking. In: 2019 IEEE international conference on multimedia and expo (ICME), pp. 31–36. IEEE (2019)
Curcic, D.: Number of academic papers published per year – WordsRated (2023). https://wordsrated.com/number-of-academic-papers-published-per-year/. Accessed 2 Oct 2023
Foster, B.: Transmission of knowledge. A Companion to the Ancient near East, pp. 261–272 (2020)
Lombard, M.D.: Professional writing, technology, and the rhizomatic transmission of knowledge. Ph.D. thesis, Purdue University (2008)
Magara, M.B., Ojo, S.O., Zuva, T.: A comparative analysis of text similarity measures and algorithms in research paper recommender systems. In: 2018 Conference on Information Communications Technology and Society (ICTAS), pp. 1–5. IEEE (2018)
Martinson, A.: Guía para la redacción de artículos científicos destinados a la publicación. Unesco París (1983)
Moya, D., Tapia, L., Albán, M., Rodríguez, G.: Un enfoque de machine learning en el desarrollo de sistema recomendadores para procesos de investigación. Rev. Ibérica Sist. Tecnol. Inform. E28, 816–827 (2020)
Nair, A.M., George, J.P., Gaikwad, S.M.: Similarity analysis for citation recommendation system using binary encoded data. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. 1–5. IEEE (2020)
Schneegans, S., Lewis, J., Straza, T.: Informe de la unesco sobre la ciencia: la carrera contra el reloj para un desarrollo más inteligente–resumen ejecutivo [internet] (2021)
To, W., Yu, B.T.: Rise in higher education researchers and academic publications. Emerald Open Res. 2, 3 (2020)
Vallejo-Huanga, D., Morillo, P., Ferri, C.: Semi-supervised Clustering Algorithms for Grouping Scientific Articles, vol. 108, pp. 325–334. Elsevier (2017)
Waheed, W., Imran, M., Raza, B., Malik, A.K., Khattak, H.A.: A hybrid approach toward research paper recommendation using centrality measures and author ranking. IEEE Access 7, 33145–33158 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vallejo-Huanga, D., Cadena, K., Villavicencio, A., Morillo, P. (2024). Implementing Similarity Matrices in a Web Interactive Tool for Scientific Articles Matching. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2024. Lecture Notes in Computer Science, vol 15158. Springer, Cham. https://doi.org/10.1007/978-3-031-71315-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-71315-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-71314-9
Online ISBN: 978-3-031-71315-6
eBook Packages: Computer ScienceComputer Science (R0)