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SciKGraph: A knowledge graph approach to structure a scientific field

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  • Tosi, Mauro Dalle Lucca
  • dos Reis, Julio Cesar

Abstract

Understanding the structure of a scientific domain and extracting specific information from it is laborious. The high amount of manual effort required to this end indicates that the way knowledge has been structured and visualized until the present day should be improved in software tools. Nowadays, scientific domains are organized based on citation networks or bag-of-words techniques, disregarding the intrinsic semantics of concepts presented in literature documents. We propose a novel approach to structure scientific fields, which uses semantic analysis from natural language texts to construct knowledge graphs. Then, our approach clusters knowledge graphs in their main topics and automatically extracts information such as the most relevant concepts in topics and overlapping concepts between topics. We evaluate the proposed model in two datasets from distinct areas. The results achieve up to 84% of accuracy in the task of document classification without using annotated data to segment topics from a set of input documents. Our solution identifies coherent keyphrases and key concepts considering the dataset used. The SciKGraph framework contributes by structuring knowledge that might aid researchers in the study of their areas, reducing the effort and amount of time devoted to groundwork.

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  • Tosi, Mauro Dalle Lucca & dos Reis, Julio Cesar, 2021. "SciKGraph: A knowledge graph approach to structure a scientific field," Journal of Informetrics, Elsevier, vol. 15(1).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:1:s175115772030626x
    DOI: 10.1016/j.joi.2020.101109
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    References listed on IDEAS

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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. van Eck, Nees Jan & Waltman, Ludo, 2014. "CitNetExplorer: A new software tool for analyzing and visualizing citation networks," Journal of Informetrics, Elsevier, vol. 8(4), pages 802-823.
    3. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    4. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    5. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    6. Garfield, Eugene, 2009. "From the science of science to Scientometrics visualizing the history of science with HistCite software," Journal of Informetrics, Elsevier, vol. 3(3), pages 173-179.
    7. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    8. Juan Ruiz-Rosero & Gustavo Ramirez-Gonzalez & Jesus Viveros-Delgado, 2019. "Software survey: ScientoPy, a scientometric tool for topics trend analysis in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1165-1188, November.
    9. McLevey, John & McIlroy-Young, Reid, 2017. "Introducing metaknowledge: Software for computational research in information science, network analysis, and science of science," Journal of Informetrics, Elsevier, vol. 11(1), pages 176-197.
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    2. Caixia Rong & Wenxue Fu, 2023. "A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses," Land, MDPI, vol. 12(8), pages 1-22, August.

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