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Using latent semantic indexing for literature based discovery

Author

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  • Michael D. Gordon
  • Susan Dumais

Abstract

Latent semantic indexing (LSI) is a statistical technique for improving information retrieval effectiveness. Here, we use LSI to assist in literature‐based discoveries. The idea behind literature‐based discoveries is that different authors have already published certain underlying scientific ideas that, when taken together, can be connected to hypothesize a new discovery, and that these connections can be made by exploring the scientific literature. We explore latent semantic indexing's effectiveness on two discovery processes: uncovering “nearby” relationships that are necessary to initiate the literature based discovery process; and discovering more distant relationships that may genuinely generate new discovery hypotheses. © 1998 John Wiley & Sons, Inc.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jamest:v:49:y:1998:i:8:p:674-685
    DOI: 10.1002/(SICI)1097-4571(199806)49:83.0.CO;2-T
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    Cited by:

    1. 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).
    2. 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.
    3. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    4. Ronald Kostoff & Raymond Koytcheff & Clifford Lau, 2008. "Structure of the nanoscience and nanotechnology applications literature," The Journal of Technology Transfer, Springer, vol. 33(5), pages 472-484, October.
    5. Kostoff, R.N. & Tshiteya, R. & Pfeil, K.M. & Humenik, J.A. & Karypis, G., 2005. "Power source roadmaps using bibliometrics and database tomography," Energy, Elsevier, vol. 30(5), pages 709-730.
    6. Justin Mower & Trevor Cohen & Devika Subramanian, 2020. "Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance," Drug Safety, Springer, vol. 43(1), pages 67-77, January.
    7. 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.
    8. Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
    9. Benito-Santos, Alejandro & Theron, Roberto, 2019. "Cross-domain Visual Exploration of Academic Corpora via the Latent Meaning of User-authored Keywords," OSF Preprints h29qv, Center for Open Science.
    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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