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Do second-order similarities provide added-value in a hybrid approach?

Author

Listed:
  • Bart Thijs

    (Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven)

  • Edgar Schiebel

    (AIT Austrian Institute of Technology GmbH)

  • Wolfgang Glänzel

    (Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven
    Department of Science Policy and Scientometrics, LHAS)

Abstract

Recent studies on first- and second-order similarities have shown that the latter one outperforms the first one as input for document clustering or partitioning applications. First-order similarities based on bibliographic coupling or on lexical approaches come with specific methodological issues like sparse matrices, sensitive to spelling variances or context differences. Second-order similarities were proposed to tackle these problems and take the lexical context into account. But also a hybrid combination of both types of similarities proved an important improvement which integrates the strengths of the two approaches and diminishes their weaknesses. In this paper we extend the notion of second-order similarity by applying it in the context of the hybrid approach. We conclude that there is no added value for the clearly defined clusters but that the second-order similarity can provide an additional viewpoint for the more general clusters.

Suggested Citation

  • Bart Thijs & Edgar Schiebel & Wolfgang Glänzel, 2013. "Do second-order similarities provide added-value in a hybrid approach?," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 667-677, September.
    • Handle: RePEc:spr:scient:v:96:y:2013:i:3:d:10.1007_s11192-012-0896-1
    DOI: 10.1007/s11192-012-0896-1
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    References listed on IDEAS

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    1. Edgar Schiebel, 2012. "Visualization of research fronts and knowledge bases by three-dimensional areal densities of bibliographically coupled publications and co-citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 557-566, May.
    2. Wolfgang Glänzel & Bart Thijs, 2012. "Using ‘core documents’ for detecting and labelling new emerging topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 399-416, May.
    3. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    4. Wolfgang Glänzel & Frizo Janssens & Bart Thijs, 2009. "A comparative analysis of publication activity and citation impact based on the core literature in bioinformatics," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 109-129, April.
    5. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    6. Alexander Kopcsa & Edgar Schiebel, 1998. "Science and technology mapping: A new iteration model for representing multidimensional relationships," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(1), pages 7-17.
    7. Wolfgang Glänzel & Bart Thijs, 2011. "Using ‘core documents’ for the representation of clusters and topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 297-309, July.
    8. Ahlgren, Per & Colliander, Cristian, 2009. "Document–document similarity approaches and science mapping: Experimental comparison of five approaches," Journal of Informetrics, Elsevier, vol. 3(1), pages 49-63.
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    Citations

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    Cited by:

    1. Bart Thijs & Lin Zhang & Wolfgang Glänzel, 2015. "Bibliographic coupling and hierarchical clustering for the validation and improvement of subject-classification schemes," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1453-1467, December.
    2. Fabian Meyer-Brötz & Edgar Schiebel & Leo Brecht, 2017. "Experimental evaluation of parameter settings in calculation of hybrid similarities: effects of first- and second-order similarity, edge cutting, and weighting factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1307-1325, June.
    3. Wolfgang Glänzel & Bart Thijs, 2017. "Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1071-1087, May.
    4. Ávila-Robinson, Alfonso & Islam, Nazrul & Sengoku, Shintaro, 2022. "Exploring the knowledge base of innovation research: Towards an emerging innovation model," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    5. Yun, Jinhyuk, 2022. "Generalization of bibliographic coupling and co-citation using the node split network," Journal of Informetrics, Elsevier, vol. 16(2).
    6. Emili Vizuete-Luciano & Oktay Güzel & José M. Merigó, 2023. "Bibliometric research of the Pay-What-You-Want Topic," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 413-426, October.
    7. Christian Weismayer & Ilona Pezenka, 2017. "Identifying emerging research fields: a longitudinal latent semantic keyword analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1757-1785, December.
    8. Nauman Majeed & Sulaiman Ainin, 2021. "Visualizing the evolution and landscape of socio-economic impact research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 637-659, April.
    9. Yun, Jinhyuk & Ahn, Sejung & Lee, June Young, 2020. "Return to basics: Clustering of scientific literature using structural information," Journal of Informetrics, Elsevier, vol. 14(4).

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