Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model
This work addresses the need for consensus on emergence attributes in AI research, though it is incremental as it builds on existing detection methods.
The study tackled the problem of early identification of emerging topics in AI by proposing a new indicator-based method that incorporates collaboration and technological impact attributes, and it successfully identified and ranked emerging topics with scores during the study period.
Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score.