Identify technologically relevant papers for 3D printing : a comparison of indicators based on scientific and patent data

Identify which scientific advances support technological innovation is a very dynamic area of study. Recent literature on this subject proposes two heterogene methods in order to answer this issue. Yamashita (2020) proposes the analysis of references and tracks patent citation while Ogawa and Kajikawa (2015) use an other approach, they make cluster of scientific articles and extract keywords for match them with patent data. They refer to two distinct conceptions of science to technology flows. Yamashita (2020) relies on the idea that the potential contribution of a scientific article comes from the proximity between prior knowledge whereas Ogawa and Kajikawa (2015) highlight a semantic proximity that suggests a simultaneous development of science and technology. In order to better understand the mechanisms of knowledge transfer and to better define the blurring of the boundary between science and technology, this article proposes to compare the predictions of these two indicators. To do so, we focus on the case of 3D printing technologies and use original data from the LENS project which make possible to link scientific articles and patents both on the dimensions of citations and authors-inventors. We relied on the DWPI patent database in order to gather complementary patent data, including texts. Based on these data, we run regressions to compare the prediction of both indicators on the citations received from patents by articles and patent published by scientific authors. Our preliminary result shows that, in the case of 3D printing, passed citations brings to lower predicting performances than the semantic similarity approach.