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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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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.

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References

  • Altshuller, G. (1984). Creativity as an exact science. The Netherlands: Gordon and Breach Science Publishers.

  • Bernstein, J. M. (1996). The role of allergy in eustachian tube blockage and Otitis media with effusion: A review. Otolaryngology-Head and Neck Surgery, 114(4), 562–568.

    Google Scholar 

  • Carey, P. (2010). Chapter 28: Meniere’s disease. Handbook of Clinical Neurophysiology, 9, 371–381.

    Article  Google Scholar 

  • Choi, S., Janghyeok, Y., Kim, K., Lee, J., & Kim, C. (2011). SAO network analysis of patents for technology trends identification: A case study of polymer electrolyte membrane in proton exchange membrane fuel cells. Scientometrics, 88, 863–883.

    Article  Google Scholar 

  • Coulet, A., Shah, N., Garten, Y., Musen, M., & Altman, R. (2010). Using text to build semantic networks for pharmacogenomics. Journal of Biomedical Informatics, 43, 1009–1019.

    Article  Google Scholar 

  • Day, G., & Shoemaker, P. (2004). Driving through the fog: Managing at the edge. Long Range Planning, 37, 127–142.

    Article  Google Scholar 

  • Feldman, R., Regev, Y., Hurviz, E., & Finkelstein-Landau, M. (2003). Mining the biomedical literature using semantic analysis and natural language processing techniques. Biosilico, 1(2), 69–80.

    Google Scholar 

  • Gordon, M., & Dumais, S. (1998). Using latent semantic indexing for literature based discovery. Journal of the American Society for Information Science, 49(8), 674–685.

    Google Scholar 

  • Kim, H., et al. (2010) Cause-and-effect function analysis. In Proceedings of the 2010 IEEE ICMIT.

  • Kim, H., & Kim, K. (2012). Causality-based function network for identifying technological analogy. Expert System with Applications, 39, 10607–10619.

    Article  Google Scholar 

  • Kostoff, R. (2006). Systematic acceleration of radical discovery and innovation in science and technology. Technological Forecasting & Social Change, 73, 923–936.

    Google Scholar 

  • Kostoff, R. (2008). Literature-related discovery (LRD): Introduction and background. Technological Forecasting and Social Change, 75, 165–185.

    Article  Google Scholar 

  • Kostoff, R. (2012). Literature-related discovery and innovation—Update. Technological Forecasting and Social Change, 79, 789–800.

    Article  Google Scholar 

  • Kostoff, R., Block, J., Solka, J., Briggs, M., Rushenberg, R., Stump, J., et al. (2008a). Literature-related discovery (LRD): Lessons learned, and future research directions. Technological Forecasting and Social Change, 75, 276–299.

    Article  Google Scholar 

  • Kostoff, R., Briggs, M., Solka, J., & Rushenberg, R. (2008b). Literature-related discovery (LRD): Methodology. Technological Forecasting and Social Change, 75, 186–202.

    Article  Google Scholar 

  • Kostoff, R., Solka, J., Rushenberg, R., & Wyatt, J. (2008c). Literature-related discovery (LRD): Water purification. Technological Forecasting and Social Change, 75, 256–275.

    Article  Google Scholar 

  • Kumar, A., Harharpreet, K., Devin, P., & Mohan, V. (2009). Role of coenzyme Q10 (CoQ10) in cardiac disease, hypertension and Meniere-like syndrome. Pharmacology & Therapeutics, 124(2009), 259–268.

    Article  Google Scholar 

  • Petric, I., Urbancic, T., Cestnik, B., & Macedoni-Luksic, M. (2009). Literature mining Rajolink for uncovering relations between biomedical concepts. Journal of Biomedical Informatics, 42, 219–227.

    Article  Google Scholar 

  • Porter, A., & Cunningham, S. (2005). Tech Mining. Hoboken, NJ: Wiley Interscience.

  • Rimell, L., & Clark, (2009). Porting a lexicalized-grammar parser to the biomedical domain. Journal of Biomedical Informatics, 42(2009), 852–865.

    Article  Google Scholar 

  • Rubin, M., Kudryavsev, A., Litvin, S., Petrov, V., et al. (2010). Operation principles of systems/collection of scientific papers. Library of TRIZ developers summit, vol 4. www.triz-summit.ru. Accessed latest June 15, 2012.

  • Sajjadi, M., & Paparella, M. (2008). Meniere’s disease. Lancet, 372, 406–414.

    Article  Google Scholar 

  • Smalheiser, N., & Swanson, D. (1998). Using Arrowsmith: A computer assisted approach to formulating and assessing scientific hypotheses. Computer Methods and Programs in Biomedicine, 57, 149–153.

    Article  Google Scholar 

  • Song, M.-K., Park, S., Alamgir, F., Cho, J., & Liu, M. (2011). Nanostructured electrodes for lithium-ion and lithium–air batteries: The latest developments, challenges and perspectives. Materials Science and Engineering R, 72, 203–252.

    Article  Google Scholar 

  • Suzuki, M., Krug, M. S., Cheng, K. C., Yazawa, Y., Bernstein, J., Kwon, S. S., et al. (2003). Antibodies against inner ear proteins in the sera of patients with inner ear diseases. International Congress Series, 1240, 1163–1167.

    Article  Google Scholar 

  • Swanson, D. (2008). Running esophageal acid reflux, and atrial fibrillation: A chain of events linked by evidence from separate medical literatures. Medical Hypotheses, 71, 178–185.

    Google Scholar 

  • Swanson, D., & Smalheiser, N. (1997). An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artificial Intelligence, 91, 183–203.

    Google Scholar 

  • Todhunter, J., et al. (2010). System and method for automatic semantic labeling of natural language texts; patent application WO-2010105216 A3.

  • Van der Eijk, C., Van Mulligen, E., Kors, J., & Mons, B. (2004). Constructing an associative concept space for literature-based discovery. Journal of American Society for Information Science and Technology, 55(5), 436–444.

    Google Scholar 

  • Verbitsky. (2004). Semantic TRIZ. triz-journal.com. http://www.triz-journal.com/archives/2004/.

  • Yoon, J., & Kim, K. (2012). Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics, 90, 445–461.

    Article  Google Scholar 

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Acknowledgments

The author would like to thank triz XXI for its sponsorship and IHS for the example to show the semantic extraction of relationships.

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Correspondence to Jose M. Vicente-Gomila.

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Vicente-Gomila, J.M. The contribution of syntactic–semantic approach to the search for complementary literatures for scientific or technical discovery. Scientometrics 100, 659–673 (2014). https://doi.org/10.1007/s11192-014-1299-2

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