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.
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Notes
AMiner: https://aminer.org.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed.
Web of Science: https://wokinfo.com.
RapidMiner: https://rapidminer.com.
scikit-learn: https://scikit-learn.org.
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Acknowledgements
This research was supported by the Klaus Tschira Stiftung gGmbH. Furthermore, the authors would also like to thank all participants of the evaluations who were primarily associated with the Helmholtz Institute for Biomedical Engineering, Aachen (Germany).
Funding
This study was funded by the Klaus Tschira Stiftung gGmbH (Grant No. 00.263.2015).
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Bukowski, M., Geisler, S., Schmitz-Rode, T. et al. Feasibility of activity-based expert profiling using text mining of scientific publications and patents. Scientometrics 123, 579–620 (2020). https://doi.org/10.1007/s11192-020-03414-8
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DOI: https://doi.org/10.1007/s11192-020-03414-8
Keywords
- Supervised learning
- Biomedical engineering domain model
- Translational value chain
- Research evaluation
- Author contribution
- Domain-specific recommendation
- Self- and external-assessment