IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v56y2003i1d10.1023_a1021954808804.html
   My bibliography  Save this article

Hypothesis generation guided by co-word clustering

Author

Listed:
  • Johannes Stegmann

    (Free University Berlin, Medical Library University Hospital Benjamin Franklin)

  • Guenter Grohmann

    (University Hospital Free University Berlin)

Abstract

Co-word analysis was applied to keywords assigned to MEDLINE documents contained in sets of complementary but disjoint literatures. In strategical diagrams of disjoint literatures, based on internal density and external centrality of keyword-containing clusters, intermediate terms (linking the disjoint partners) were found in regions of below-median centrality and density. Terms representing the disjoint literature themes were found in close vicinity in strategical diagrams of intermediate literatures. Based on centrality-density ratios, characteristic values were found which allow a rapid identification of clusters containing possible intermediate and disjoint partner terms. Applied to the already investigated disjoint pairs Raynaud"s Disease - Fish Oil, Migraine - Magnesium, the method readily detected known and unknown (but relevant) intermediate and disjoint partner terms. Application of the method to the literature on Prions led to Manganese as possible disjoint partner term. It is concluded that co-word clustering is a powerful method for literature-based hypothesis generation and knowledge discovery.

Suggested Citation

  • Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
  • Handle: RePEc:spr:scient:v:56:y:2003:i:1:d:10.1023_a:1021954808804
    DOI: 10.1023/A:1021954808804
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1021954808804
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1023/A:1021954808804?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael D. Gordon & Susan Dumais, 1998. "Using latent semantic indexing for literature based discovery," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(8), pages 674-685.
    2. Neal Coulter & Ira Monarch & Suresh Konda, 1998. "Software engineering as seen through its research literature: A study in co‐word analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(13), pages 1206-1223.
    3. Michael D. Gordon & Robert K. Lindsay, 1996. "Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(2), pages 116-128, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Senator Jeong & Hong-Gee Kim, 2010. "Intellectual structure of biomedical informatics reflected in scholarly events," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(2), pages 541-551, November.
    2. Matteo Lascialfari & Marie-Benoît Magrini & Guillaume Cabanac, 2022. "Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6165-6189, November.
    3. Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    4. Daniele Rotolo & Loet Leydesdorff, 2014. "Matching MEDLINE/PubMed Data with Web of Science (WOS): A Routine in R language," SPRU Working Paper Series 2014-14, SPRU - Science Policy Research Unit, University of Sussex Business School.
    5. Leydesdorff, Loet & Welbers, Kasper, 2011. "The semantic mapping of words and co-words in contexts," Journal of Informetrics, Elsevier, vol. 5(3), pages 469-475.
    6. Gaston Heimeriks & Ron Boschma, 2014. "The path- and place-dependent nature of scientific knowledge production in biotech 1986–2008," Journal of Economic Geography, Oxford University Press, vol. 14(2), pages 339-364.
    7. Babak Amiri & Ramin Karimianghadim & Navid Yazdanjue & Liaquat Hossain, 2021. "Research topics and trends of the hashtag recommendation domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2689-2735, April.
    8. Ehsan Mohammadi, 2012. "Knowledge mapping of the Iranian nanoscience and technology: a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(3), pages 593-608, September.
    9. Shanshan Wang & Junping Qiu & Jia Zhou & Yunlong Yu, 2022. "Evolution and Future Prospects of Education Evaluation Research in China over the Last Decade," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    10. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.
    2. Santana, Monica & Cobo, Manuel J., 2020. "What is the future of work? A science mapping analysis," European Management Journal, Elsevier, vol. 38(6), pages 846-862.
    3. Chang-Ping Hu & Ji-Ming Hu & Sheng-Li Deng & Yong Liu, 2013. "A co-word analysis of library and information science in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 369-382, November.
    4. De Andrés Fazio, Salvador & Urquía Grande, Elena & Pérez Estébanez, Raquel, 2022. "The “secret life” of the Statement of Cash Flow: A bibliometric analysis," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).
    5. Joel O. Botai & Christina M. Botai & Katlego P. Ncongwane & Sylvester Mpandeli & Luxon Nhamo & Muthoni Masinde & Abiodun M. Adeola & Michael G. Mengistu & Henerica Tazvinga & Miriam D. Murambadoro & S, 2021. "A Review of the Water–Energy–Food Nexus Research in Africa," Sustainability, MDPI, vol. 13(4), pages 1-26, February.
    6. Jose M. Vicente-Gomila, 2014. "The contribution of syntactic–semantic approach to the search for complementary literatures for scientific or technical discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 659-673, September.
    7. Hanen Khaldi & Vicente Prado-Gascó, 2021. "Bibliometric maps and co-word analysis of the literature on international cooperation on migration," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1845-1869, October.
    8. Jinkai Yu & Wenjing Bi, 2019. "Evolution of Marine Environmental Governance Policy in China," Sustainability, MDPI, vol. 11(18), pages 1-14, September.
    9. Li, Qing & Zhang, Huaige & Hong, Xianpei, 2020. "Knowledge structure of technology licensing based on co-keywords network: A review and future directions," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 154-165.
    10. Alan L. Porter & Alisa Kongthon & Jye-Chyi (JC) Lu, 2002. "Research profiling: Improving the literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(3), pages 351-370, March.
    11. Gamal Crichton & Simon Baker & Yufan Guo & Anna Korhonen, 2020. "Neural networks for open and closed Literature-based Discovery," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    12. Sung Kim & Derek Hansen & Richard Helps, 2018. "Computing research in the academy: insights from theses and dissertations," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 135-158, January.
    13. Choudhury, Nazim & Faisal, Fahim & Khushi, Matloob, 2020. "Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction," Journal of Informetrics, Elsevier, vol. 14(3).
    14. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    15. Mónica Santana & Rafael Morales-Sánchez & Susana Pasamar, 2020. "Mapping the Link between Corporate Social Responsibility (CSR) and Human Resource Management (HRM): How Is This Relationship Measured?," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    16. Wei Zhang & Qingpu Zhang & Bo Yu & Limei Zhao, 2015. "Knowledge map of creativity research based on keywords network and co-word analysis, 1992–2011," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1023-1038, May.
    17. Jiangang Shi & Kaifeng Duan & Guangdong Wu & Hongyun Si & Rui Zhang, 2022. "Sustainability at the community level: A bibliometric journey around a set of sustainability‐related terms," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 256-274, February.
    18. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    19. Yan Nan & Tieying Feng & Yuqun Hu & Xinzhu Qi, 2020. "Understanding Aging Policies in China: A Bibliometric Analysis of Policy Documents, 1978–2019," IJERPH, MDPI, vol. 17(16), pages 1-15, August.
    20. Christian Sternitzke, 2009. "Patents and publications as sources of novel and inventive knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 551-561, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:56:y:2003:i:1:d:10.1023_a:1021954808804. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.