Trace of Knowledge: Benchmarking Novel Text Mining Based Measurements

The impact of public research outcomes on economies, and societies, in particular, in terms of innovation and development is widely accepted and empirically investigated [9, 3]. However, many studies suggest a systematic underestimation of the impact and benefits of public research. Empirical studies describe that current approaches capture only specific aspects of knowledge transfer between public research institutions and private entities [1, 4, 7]. The main interrelated reasons contributing to this systematic underestimation are that most established knowledge transfer measurements focus on intermediaries and use proxyindicators like patents, licenses, spin-outs and co-publications as data sources, but these metrics are problematic because they can result in type I and type II errors, since many of them capture a transfer that is never utilized by a private entity (e.g. like unused patents) [8]. In addition, there are occasions where the proxy is not met so the actual use is not being captured. We try to improve this systematic underestimation by adapting novel computer linguistics methods to this field and putting them into perspective with the existing measures of knowledge transfer. We use both basic and more advanced statistical learning tools from the field of computational linguistics and statistical learning to trace the knowledge fragments[2, 6]. In addition, we utilize a mixture of standard algebraic and probabilistic methods. Furthermore, pattern recognition, classification algorithms help to trace the public research outcomes, going beyond plain word co-occurrence[10, 5]. We identify concrete public research outcomes that identify their transfer and transmission channels on several levels, which represent different forms of knowledge utilization. Also, we trace the public research outcomes within patents, company websites, annual reports, among others. Next, we compare our results with established measures such as non-patent references that are added to patents and are referring to prior scientific research used for the invention. The aim is to provide additional measures to help identify diverse types of research knowledge and to demonstrate specific circumstances in which these novel measures might be beneficial. Also, we identify limitations and restrictions that need to be taken into consideration when assessing the impact of public research on our knowledge transfer measurements. The outcome of this study contributes to the understanding about knowledge transfer in multiple ways. Firstly, assessing the effectiveness of using novel measurements of knowledge transfer from university research to patents (e.g. IP regulated inventions) may potentially improve upon the current use of prior-Art citations as proxy-indicators. Secondly, we can detect what type of research knowledge is transferred to companies that are in proximity to a particular university. Thirdly, we trace whether patents are actually utilized in companies, and finally we identify how much knowledge actually mitigates from universities to patents, and then to the commercial portfolios o companies. These outcomes will provide further empirical evidence to clarify notions about the benefits public research has on the socioeconomic environment; whether the research mostly contributes to novel ideas and impulses for innovation or rather by solving existing problems. We plan to benchmark our novel computational methods against the long established proxyindicators to assess the extend to which this novel approach might benefit the contemporary empirical study of knowledge transfer between universities and industry. Jelc des: O32,O33 Trace of Knowledge: Benchmarking Novel Text Mining Based Measurements