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Bibliographic coupling, common abstract stems and clustering: A comparison of two document-document similarity approaches in the context of science mapping

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  • Per Ahlgren

    (Swedish School of Library and Information Science)

  • Bo Jarneving

    (Swedish School of Library and Information Science)

Abstract

This paper deals with two document-document similarity approaches in the context of science mapping: bibliographic coupling and a text approach based on the number of common abstract stems. We used 43 articles, published in the journal Information Retrieval, as test articles. An information retrieval expert performed a classification of these articles. We used the cosine measure for normalization, and the complete linkage method was used for clustering the articles. A number of articles pairs were ranked (1) according to descending normalized coupling strength, and (2) according to descending normalized frequency of common abstract stems. The degree of agreement between the two obtained rankings was low, as measured by Kendall’s tau. The agreement between the two cluster solutions, one for each approach, was fairly low, according to the adjusted Rand index. However, there were examples of perfect agreement between the coupling solution and the stems solution. The classification generated by the expert contained larger groups compared to the coupling and stems solutions, and the agreement between the two solutions and the classification was not high. According to the adjusted Rand index, though, the stems solution was a better approximation of the classification than the coupling solution. With respect to cluster quality, the overall Silhouette value was slightly higher for the stems solution. Examples of homogeneous cluster structures, as well as negative Silhouette values, were found with regard to both solutions. The expert classification indicates that the field of information retrieval, as represented by one volume of articles published in Information Retrieval, is fairly heterogeneous regarding research themes, since the classification is associated with 15 themes. The complete linkage method, in combination with the upper tail rule, gave rise to a fairly good approximation of the classification with respect to the number of identified groups, especially in case of the stems approach.

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  • Per Ahlgren & Bo Jarneving, 2008. "Bibliographic coupling, common abstract stems and clustering: A comparison of two document-document similarity approaches in the context of science mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 273-290, August.
    • Handle: RePEc:spr:scient:v:76:y:2008:i:2:d:10.1007_s11192-007-1935-1
    DOI: 10.1007/s11192-007-1935-1
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    References listed on IDEAS

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    1. M. M. Kessler, 1965. "Comparison of the results of bibliographic coupling and analytic subject indexing," American Documentation, Wiley Blackwell, vol. 16(3), pages 223-233, July.
    2. H. P. F. Peters & R. R. Braam & A. F. J. van Raan, 1995. "Cognitive resemblance and citation relations in chemical engineering publications," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 46(1), pages 9-21, January.
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    Cited by:

    1. Ping Liu & Qiong Wu & Xiangming Mu & Kaipeng Yu & Yiting Guo, 2015. "Detecting the intellectual structure of library and information science based on formal concept analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 737-762, September.
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    3. Vilker Zucolotto Pessin & Luciana Harue Yamane & Renato Ribeiro Siman, 2022. "Smart bibliometrics: an integrated method of science mapping and bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3695-3718, June.
    4. Cristian Colliander & Per Ahlgren, 2012. "Experimental comparison of first and second-order similarities in a scientometric context," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 675-685, February.
    5. Sandra Boric & Edgar Schiebel & Christian Schlogl & Michaela Hildebrandt & Christina Hofer & Doris M. Macht, 2021. "Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 7(5), pages 27-44, December.
    6. Yuen-Hsien Tseng & Ming-Yueh Tsay, 2013. "Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 503-528, May.
    7. Minh Nguyen Quang & Tim Rogers & Jan Hofman & Ana B Lanham, 2019. "New framework for automated article selection applied to a literature review of Enhanced Biological Phosphorus Removal," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-42, May.
    8. Nicolaisen, Jeppe & Frandsen, Tove Faber, 2012. "Consensus formation in science modeled by aggregated bibliographic coupling," Journal of Informetrics, Elsevier, vol. 6(2), pages 276-284.
    9. Xia Gao & Jiancheng Guan, 2009. "Networks of scientific journals: An exploration of Chinese patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 283-302, July.
    10. 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.
    11. Yuen-Hsien Tseng & Chun-Yen Chang & M. Shane Tutwiler & Ming-Chao Lin & James P. Barufaldi, 2013. "A scientometric analysis of the effectiveness of Taiwan’s educational research projects," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1141-1166, June.

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