Tom Magerman

Developing Technology in the Vicinity of Science: An Examination of the Relationship Between Science Intensity (of Patents) and Technological Productivity within the Field of Biotechnology

In this paper we investigate — at a country level — the relationship between the science intensity of patents and technological productivity, taking into account differences in terms of scientific productivity. The number of non patent references in patents is considered as an approximation of the science intensity of technology whereas a country’s technological and scientific performance is measured in terms of productivity (i.e., number of patents and publications per capita). We use USPTO patent-data pertaining to biotechnology for 20 countries covering the time period 1992–1999. Our findings reveal mutual positive relationships between scientific and technological productivity for the respective countries involved. At the same time technological productivity is associated positively with the science intensity of patients. These results are confirmed when introducing time effects. These observations corroborate the construct validity of science intensity as a distinctive indicator and suggest its usefulness for assessing science and technology dynamics.

Assessment of Latent Semantic Analysis (LSA) Text Mining Algorithms for Large Scale Mapping of Patent and Scientific Publication Documents

In this study we conduct a thorough assessment of the LSA text mining method and its options (preprocessing, weighting, …) to grasp similarities between patent documents and scientific publications to develop a new method to detect direct science-technology linkages - as this is instrumental for research on topics in innovation management, e.g. anticommons issues. We want to assess effectiveness (in terms of precision and recall) and derive best practices on weighting and dimensionality reduction for application on patent data. We use LSA to derive similarity from a large set of patent and scientific publication documents (88,248 patent documents and 948,432 scientific publications) based on 40 similarity measurement variants (four weighting schemas are combined with ten levels of dimensionality reduction and the cosine metric). A thorough validation is set up to compare the performance of those measure variants (expert validation of 300 combinations plus a control set of 30,000 patents). We do not find evidence for the claims of LSA to be superior to plain cosine measures or simple common term or co-occurrence based measures in our data; dimensionality reduction only seems to approach cosine measures applied on the full vector space. We propose the combination of two measures based on the number of common terms (weighted by the minimum of the number of terms of both documents and weighted by the maximum of the number of terms of both documents respectively) as a more robust method to detect similarity between patents and publications.