Center for R and nitoring

Leuven, Belgium

Center for R and nitoring

Leuven, Belgium
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Abdulhayoglu M.A.,Center for R and nitoring | Abdulhayoglu M.A.,Catholic University of Leuven | Thijs B.,Center for R and nitoring
CEUR Workshop Proceedings | Year: 2017

The objective of this study is to find the most appropriate parameters and text components for item-wise matching the two large bibliographic datasets: Clarivate Analytics Web of Science (WoS) and Elsevier's Scopus. Our focus is on detecting exact matches, that is, no false positives are tolerated at all. To this end, we follow a twofold matching procedure. First, a locality sensitive hashing (LSH) algorithm [15] is applied, which provides fast approximate nearest neighbours and similarities, in order to obtain WoS-Scopus pair suggestions. We experiment with three different combinations of text components (i.e., only publication titles, titles + journal names, co-author names + titles + journals) as input for the matching process. In addition, different values for LSH input parameters (i.e., number of random vectors, number of different random vector sets, number of neighbours, similarity threshold) are tested. Second, for each suggested pair, different heuristics are applied to identify those pair of records that indeed refer to the same publication. For example, the pairs are classified as correct matches if the journal name, volume, issue and begin page do match. We achieved the best results when only titles were matched and 50-50-50-0.80 or 100-30-30-0.80 input parameters are used. We observe that at least 70% of WoS publications are also indexed by Scopus. Last but not the least, when the parameters leading to the best matching results were applied, it took just about an hour to match 1.6 million vs 2.2 million. © 2017, CEUR-WS. All rights reserved.


Van Looy B.,Center for R and nitoring | Van Looy B.,Research Division INCENTIM | Landoni P.,Polytechnic of Milan | Callaert J.,Center for R and nitoring | And 3 more authors.
Research Policy | Year: 2011

The phenomenon of entrepreneurial universities has received considerable attention over the last decades. An entrepreneurial orientation by academia might put regions and nations in an advantageous position in emerging knowledge-intensive fields of economic activity. At the same time, such entrepreneurial orientation requires reconciliation with the scientific missions of academia. Large-scale empirical research on antecedents of the entrepreneurial effectiveness of universities is scarce. This contribution examines the extent to which scientific productivity affect entrepreneurial effectiveness, taking into account the size of universities and the presence of disciplines, as well as the R&D intensity of the regional business environment (BERD). In addition, we assess the occurrence of trade-offs between different transfer mechanisms (contract research, patenting and spin off activity). The data used pertain to 105 European universities. Our findings reveal that scientific productivity is positively associated with entrepreneurial effectiveness. Trade-offs between transfer mechanisms do not reveal themselves; on the contrary, contract research and spin off activities tend to facilitate each other. Limitations and implications for future research are discussed. © 2011 Elsevier B.V. All rights reserved.


Zhang L.,Center for R and nitoring | Zhang L.,Dalian University of Technology | Janssens F.,Center for R and nitoring | Janssens F.,Catholic University of Leuven | And 4 more authors.
Scientometrics | Year: 2010

The objective of this study is to use a clustering algorithm based on journal cross-citation to validate and to improve the journal-based subject classification schemes. The cognitive structure based on the clustering is visualized by the journal cross-citation network and three kinds of representative journals in each cluster among the communication network have been detected and analyzed. As an existing reference system the 15-field subject classification by Glänzel and Schubert (Scientometrics 56:55-73, 2003) has been compared with the clustering structure. © 2010 Akadémiai Kiadó, Budapest, Hungary.


Magerman T.,Center for R and nitoring | Magerman T.,Catholic University of Leuven | van Looy B.,Center for R and nitoring | van Looy B.,Catholic University of Leuven | And 2 more authors.
Scientometrics | Year: 2010

In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detected-and hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents-in this case, patents and publications-might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n = 6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that text mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed. © 2009 Akadémiai Kiadó, Budapest, Hungary.

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