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

摘要

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.