Feasibility of activity-based expert profiling using text mining of scientific publications and patents

Mark Bukowski (), Sandra Geisler, Thomas Schmitz-Rode and Robert Farkas
Additional contact information
Mark Bukowski: University Hospital Aachen, RWTH Aachen University
Sandra Geisler: Fraunhofer Institute for Applied Information Technology FIT
Thomas Schmitz-Rode: University Hospital Aachen, RWTH Aachen University
Robert Farkas: University Hospital Aachen, RWTH Aachen University

Scientometrics, 2020, vol. 123, issue 2, No 2, 579-620

Abstract: Abstract Research and development (R&D) in many technological areas is characterized by growing complexity. In biomedical engineering, too, interdisciplinary collaboration is regarded as a promising way to master this challenge. Therefore, identifying suitable experts becomes crucial, which is currently being researched, amongst others, by analyzing semantic data. However, previous approaches lack clarity and traceability of the mechanisms for compiling top-n lists of recommended experts, as domain specificity in profiling is insufficient. Moreover, these recommenders are mainly based on scientific publications, while patents are rarely considered as an important outcome of R&D. Thus, we study the feasibility of profiling 16 biomedical engineering experts using both publications and patents. These documents are automatically labeled according to a three-dimensional domain model by machine learning-based classifiers. On this basis, we created various activity-based representations, including author-contribution-weighting. We evaluated the profiling through self- and external-assessments and tested the recommendation compared to scientometric measures in three case studies. All interviewed experts identify themselves among 10 pseudonymous profiles and 96% of all 51 external-assignments are correct. The recommendation over three case studies reaches a high mean average precision of 89% and contrasts with the use of scientometric measures (41%). Moreover, the activity based on patents primarily corresponds to that of publications but patents also introduce new activities. The author-contribution-weighting improves the performance. In conclusion, our findings show that exploiting publications and patents enables comprehensible profiling of biomedical engineering experts that allows visual comparisons and clear selection and ranking of potential R&D collaboration partners along the translational value chain.

Keywords: Supervised learning; Biomedical engineering domain model; Translational value chain; Research evaluation; Author contribution; Domain-specific recommendation; Self- and external-assessment; 68T50; 68P10; 68P20; 68U15; 68U35 (search for similar items in EconPapers)
JEL-codes: C38 C89 C99 D83 I19 O32 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03414-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:123:y:2020:i:2:d:10.1007_s11192-020-03414-8

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-020-03414-8

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().