Name
Abstract | ||
|---|---|---|
In this paper, we present a system for exploring the temporal trends of scientific concepts. Scientific concepts were captured by extracting noun phrases and entities from all computer science papers of arXiv.org. Our system allows users to review the time series of numerous concepts and to identify positively and negatively trending concepts. By applying clustering techniques and cluster analysis visualizations, it can also present concepts which share the same usage patterns over time. Our system can be beneficial for both ordinary researchers of any field and for researchers working in bibliometrics and scientometrics in order to investigate the evolution of scientific concepts.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JCDL.2019.00108 | JCDL |
Keywords | Field | DocType |
trend detection, scholarly data, bibliometrics, time series | Noun phrase,Information retrieval,Trend detection,Computer science,Bibliometrics,Scientometrics,Cluster analysis | Conference |
ISSN | ISBN | Citations |
2575-7865 | 978-1-7281-1547-4 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Färber | 1 | 56 | 22.11 |
| Chifumi Nishioka | 2 | 0 | 0.34 |
| Adam Jatowt | 3 | 903 | 106.73 |