Name
Abstract | ||
|---|---|---|
Human creativity is the ultimate driving force behind all of scientific progress. Indeed, the building blocks of innovations are often embodied in existing knowledge; yet, it is creativity that blends seemingly disparate knowledge. Prior work has made striding advances in quantifying the creativity of scientific papers by investigating their citation relationships. However, little is known hitherto about the underlying mechanisms governing such creative processes, largely due to that a paper's references, at best, only partially reflect its authors' actual information consumption. This work represents an initial step towards fine-granular understanding of the creative processes in scientific enterprise. In specific, using two web-scale longitudinal datasets (120.1 million papers and 53.5 billion web requests spanning 4 years), we directly contrast authors' information consumption behaviors against their knowledge products. We find that of 59.0% of the papers across all scientific fields, 25.7% of their creativity can be readily explained by the information consumed by their authors. Further, by leveraging these findings, we develop a first-of-its-kind predictive framework that accurately identifies the most critical knowledge to foster target scientific innovations. We believe that our framework is of fundamental importance to the study of scientific creativity. It promotes strategies to stimulate creative processes and provides insights towards more effective designs of information recommendation platforms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2983323.2983820 | ACM International Conference on Information and Knowledge Management |
Keywords | DocType | Volume |
Computational creativity,Measurement-driven modeling,Information recommendation | Journal | abs/1612.01450 |
ISBN | Citations | PageRank |
978-1-4503-4073-1 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyang Zhang | 1 | 23 | 5.20 |
| Dashun Wang | 2 | 627 | 27.09 |
| Ting Wang | 3 | 664 | 65.43 |