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Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities

Author

Listed:
  • Ansgar Moeller

    (University of Bremen)

  • Martin G. Moehrle

    (University of Bremen)

Abstract

Patent search is a substantial basis for many operational questions and scientometric evaluations. We consider it as a sequence of distinct stages. The “patent wide search” involves a definition of system boundaries by means of classifications and a keyword search producing a patent set with a high recall level (see Schmitz in Patentinformetrie: Analyse und Verdichtung von technischen Schutzrechtsinformationen, DGI, Frankfurt (Main), 2010 with an overview of searchable patent meta data). In this set of patents a “patent near search” takes place, producing a patent set with high(er) precision. Hence, the question arises how the researcher has to operate within this patent set to efficiently identify patents that contain paraphrased descriptions of the sought inventive elements in contextual information and whether this produces different results compared to a conventional search. We present a semiautomatic iterative method for the identification of such patents, based on semantic similarity. In order to test our method we generate an initial dataset in the course of a patent wide search. This dataset is then analyzed by means of the semiautomatic iterative method as well as by an alternative method emulating the conventional process of keyword refinement. It thus becomes obvious that both methods have their particular “raison d’être”, and that the semiautomatic iterative method seems to be able to support a conventional patent search very effectively.

Suggested Citation

  • Ansgar Moeller & Martin G. Moehrle, 2015. "Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 77-96, January.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1446-9
    DOI: 10.1007/s11192-014-1446-9
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    References listed on IDEAS

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    2. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
    3. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
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    5. Martin G Moehrle & Irina Pfennig & Jan M Gerken, 2017. "Identifying Lead Users In A B2b Environment Based On Patent Analysis — The Case Of The Crane Industry," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-20, August.

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