Generating novel research ideas using computational intelligence: A case study involving fuel cells and ammonia synthesis

Takaya Ogawa and Yuya Kajikawa

Technological Forecasting and Social Change, 2017, vol. 120, issue C, 41-47

Abstract: We proposed a method to help researchers create novel research ideas using bibliometrics. Different concepts and techniques exist in different research areas, and when the fields are sufficiently similar, a salient combination of two different areas can lead to the development of novel research. We have assumed that two different research areas, sharing a high number of similar keywords, would be excellent candidates for integration. We combined link mining and text mining techniques to elucidate hidden but implicit opportunities among apparent, explicit research clusters. To demonstrate the effectiveness of our approach, we conducted a case study on fuel cells and ammonia synthesis. Fuel cells are a rapidly growing research field, while ammonia synthesis is relatively mature. Our results successfully extracted a plausible and post-mature research idea.

Keywords: R&D management; Bibliometrics; Keyword similarity; Ammonia synthesis; Fuel cell (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S004016251730464X
Full text for ScienceDirect subscribers only

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:eee:tefoso:v:120:y:2017:i:c:p:41-47

DOI: 10.1016/j.techfore.2017.04.004

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().