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PARMA. A full text search based method for matching non-patent literature citations with scientific reference databases. A pilot study.

MPS-Authors
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Knaus,  Johannes
Big Data Analytics Group, Max Planck Digital Library, Max Planck Society;

/persons/resource/persons96338

Palzenberger,  Margit
Big Data Analytics Group, Max Planck Digital Library, Max Planck Society;

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mpdl_rio_parma_techrep_201802_supp01.xlsx
(Supplementary material), 36KB

mpdl_rio_parma_techrep_201802_supp02.txt
(Supplementary material), 762KB

mpdl_rio_parma_techrep_201802_supp03.xml
(Supplementary material), 2KB

mpdl_rio_parma_techrep_201802_supp04.xml
(Supplementary material), 2KB

Citation

Knaus, J., & Palzenberger, M. (2018). PARMA. A full text search based method for matching non-patent literature citations with scientific reference databases. A pilot study. doi:10.17617/2.2540157.


Cite as: https://hdl.handle.net/21.11116/0000-0000-70A4-8
Abstract
Patent databases contain large amounts of (almost) unstructured references to non-patent literature (NPL). To identify these references is a general research request, as they are an important indicator for determining and quantifying various relationships between science and industry. In the present pilot study, we introduce a Patent reference matching method (PARMA) that is able to process a wide range of patent records by using a combination of full text search technology with filtering and matching routines in an RDBMS. Results show that the approach establishes a solid foundation for future analytic studies on the topic.