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Two medical literatures that are logically but not bibliographically connected

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  • Don R. Swanson

Abstract

This study demonstrates that certain unintended logical connections within the scientific literature, connections potentially revealing of new knowledge, are unmarked by reference citations or other bibliographic clues. Specifically, 25 biomedical articles central to the argument that dietary fish oil causes certain blood changes are compared with 34 articles on how similar blood changes might ameliorate Raynaud's disease. The two groups of articles are thus connected by a chain of reasoning implicitly suggesting that dietary fish oil might benefit Raynaud patients, an hypothesis not heretofore published explicitly. By retrieving and bringing together these two literatures, that implicit, unstated, and perhaps unnoticed hypothesis becomes apparent. The more general problem is posed of whether systematic search techniques for bringing together logically connected literatures can be developed and described, in the hope of discovering other implicit, unstated hypotheses. The example analyzed shows that the problem, while solved in this case by trial‐and‐error search methods, may be inherently and peculiarly difficult because there are virtually no references in either literature to the other, nor are there any clues from cocitation, bibliographic coupling, or statistical association of descriptors that the two literatures are logically related. © 1987 John Wiley & Sons, Inc.

Suggested Citation

  • Don R. Swanson, 1987. "Two medical literatures that are logically but not bibliographically connected," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 38(4), pages 228-233, July.
  • Handle: RePEc:bla:jamest:v:38:y:1987:i:4:p:228-233
    DOI: 10.1002/(SICI)1097-4571(198707)38:43.0.CO;2-G
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    Cited by:

    1. Ronald N. Kostoff, 2014. "Literature-related discovery: common factors for Parkinson’s Disease and Crohn’s Disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 623-657, September.
    2. 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.
    3. 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).
    4. Mingchun Cao & Ilan Alon, 2020. "Intellectual Structure of the Belt and Road Initiative Research: A Scientometric Analysis and Suggestions for a Future Research Agenda," Sustainability, MDPI, vol. 12(17), pages 1-40, August.
    5. Christian Sternitzke, 2009. "Patents and publications as sources of novel and inventive knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 551-561, June.
    6. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.
    7. Kyebambe, Moses Ntanda & Cheng, Ge & Huang, Yunqing & He, Chunhui & Zhang, Zhenyu, 2017. "Forecasting emerging technologies: A supervised learning approach through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 236-244.
    8. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    9. Belussi, Fiorenza & Orsi, Luigi & Savarese, Maria, 2019. "Mapping Business Model Research: A Document Bibliometric Analysis," Scandinavian Journal of Management, Elsevier, vol. 35(3).
    10. Chihmao Hsieh, 2011. "Explicitly searching for useful inventions: dynamic relatedness and the costs of connecting versus synthesizing," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 381-404, February.

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