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The contribution of syntactic–semantic approach to the search for complementary literatures for scientific or technical discovery

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  • Jose M. Vicente-Gomila

    (Universitat Politêcnica de Valencia)

Abstract

The present paper tries to show that the current state of the art in syntactics and semantics, in computer systems based on the theory of inventive problem solving known as TRIZ, may help in the task of literature based discovery. With a structured and logic cause linkage between concepts, LBD could be faster and with less expert involvement at the beginning of the LBD process. The author tries to demonstrate the concept with two different problems: the hearing and balance problem known as Meniere’s disease, and to some of the current problems in the lithium air batteries for electric vehicles. By using open literature based discovery from An to Bn and from Bn to Cn, and with the logic relationships of real causes and effects approach, the author finds several relative new concepts such as vitamin A. Other concepts as niacin or fish oil, are also found, as potential to help in the Meniere’s disease. Secondly, using such procedure the author is able to find patents from disparate domain of expertise, as patents about odor control or metal casting.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1299-2
    DOI: 10.1007/s11192-014-1299-2
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    References listed on IDEAS

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    1. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
    2. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    3. 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.
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    Cited by:

    1. Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    3. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.

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    More about this item

    Keywords

    Literature based discovery; LBD; Semantic TRIZ; Syntatic–semantic processing; Menière’s disease; Tech Mining;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I10 - Health, Education, and Welfare - - Health - - - General

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