ACM Home Page
Please provide us with feedback. Feedback
Delineating the citation impact of scientific discoveries
Full text PdfPdf (1.88 MB)
Source
International Conference on Digital Libraries archive
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries table of contents
Vancouver, BC, Canada
SESSION: Visualization table of contents
Pages: 19 - 28  
Year of Publication: 2007
ISBN:978-1-59593-644-8
Authors
Chaomei Chen  Drexel University, Philadelphia, PA
Jian Zhang  Drexel University, Philadelphia, PA
Weizhong Zhu  Drexel University, Philadelphia, PA
Michael Vogeley  Drexel University, Philadelphia, PA
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 76,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1255175.1255179
What is a DOI?

ABSTRACT

Identifying the significance of specific concepts in the diffusion of scientific knowledge is a challenging issue concerning many theoretical and practical areas. We introduce an innovative visual analytic approach to integrate microscopic and macroscopic perspectives of a rapidly growing scientific knowledge domain. Specifically, our approach focuses on statistically unexpected phrases extracted from unstructured text of titles and abstracts at the microscopic level in association with the magnitude and timeliness of their citation impact at the macroscopic level. The H-index, originally defined to measure individual scientists. productivity in terms of their citation profiles, is extended in two ways: 1) to papers and terms as a means of dividing these items into two groups so as to replace the less optimal threshold-based divisions, and 2) to take into account the timeliness of the impact of knowledge diffusion in terms of the timing of citations and publications so that attention is particularly drawn towards potentially significant and timely papers. The selected terms are connected to higher-level performance indicators, such as measures derived from the H-index, in the form of decision trees. A top-down traversal of such decision trees provides an intuitive walkthrough of concepts and phrases that may underline potentially significant but currently still latent scientific discoveries. Timeliness measures can also help to identify institutions that are at the forefront of a research field. We illustrate how widely accessible tools such as Google Earth can be utilized to disseminate such insights. The practical significance for digital libraries and fostering scientific discoveries is demonstrated through the astronomical literature related to the Sloan Digital Sky Survey (SDSS).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Abt, H. A. Astronomical publication in the near future. Publications of the Astronomical Society of the Pacific, 112, (2000), 1417--1420.
 
2
Abt, H. A. Do important papers produce high citation counts. Scientometrics, 48, (2000), 65--70.
 
3
Abt, H. A. Some trends in American astronomical publications. Publications of the Astronomical Society of the Pacific, 553, (1981), 269--272.
 
4
Abt, H. A. Why some papers have long citation lifetimes. Nature, 395, (1998), 756--757.
 
5
Ackermann, E. Indicators of failed information epidemics in the scientific journal literature: A publication analysis of Polywater and Cold Nuclear Fusion. Scientometrics, 66, 3 (2006), 15.
 
6
Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A. and Vicsek, T. Evolution of the social network of scientific collaborations. Physica A, 311, (2002), 590--614.
 
7
Bush, V. As we may think. The Atlantic Monthly, 176, 1 (1945), 101--108.
 
8
 
9
 
10
Chen, C. Searching for intellectual turning points: Progressive Knowledge Domain Visualization. Proc. Natl. Acad. Sci. USA, 101, Suppl. (2004), 5303--5310.
 
11
Chen, C., Ibekwe-SanJuan, F., SanJuan, E. and Weaver, C., Visual Analysis of Conflicting Opinions. in Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST), (Baltimore, MA, 2006), 2006, 59--66.
 
12
Daim, T. U., Rueda, G., Martin, H. and Gerdsri, P. Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73, (2006), 31.
 
13
Davoust, E. and Schmadel, L. D. A study of the publishing activity of astronomers since 1969. Scientometrics, 22, (1991), 9--39.
 
14
Dunbar, K. Concept discovery in a scientific domain. Cognitive Science, 17, (1993), 397--434.
 
15
 
16
Fernandez, J. A. The transition from an individual science to a collective one: The case of astronomer. Scientometrics, 42, (1998), 61--74.
 
17
 
18
Hargens, L. L. Using the Literature: Reference Networks, Reference Contexts, and the Social Structure of Scholarship. American Sociological Review, 65, 6 (2000), 846--865.
 
19
Hirsch, J. E. An index to quantify an individual's scientific research output. PNAS, 102, (2005), 16569--16572.
20
 
21
Klinkenberg, R. and Renz, I., Adaptive information filtering: learning in the presence of concept drifts. in Learning for Text Categorization, (Menlo Park, CA, 1998), AAAI Press, 1998, 33--40.
 
22
Kostoff, R. N. Systematic acceleration of radical discovery and innovation in science and technology. Technological Forecasting and Social Change, 73, (2006), 13.
23
 
24
 
25
Meadows, A. J. and O'Connor, J. G. Bibliographical statistics as a guide to growth points in science. Science Studies, 1, 1 (1971), 95--99.
26
 
27
Newman, M., The structure of scientific collaboration networks. in Natl. Acad. Sci, (USA, 2001b), 2001b, 404--409.
 
28
 
29
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. and Parisi, D. Defining and identifying communities in networks arXiv: cond- mat/ 0309488 v1, 2003.
 
30
Root-Bernstein, R. S. Discovering: Inventing and solving problems at the frontiers of scientific knowledge. Harvard University Press, Cambridge, 1989.
 
31
Sidiropoulos, A., Katsaros, D. and Manolopoulos, Y. Generalized h-index for disclosing latent facts in citation networks. arXiv:cs.DL/0607066 (2006).
 
32
Simon, H. A., Langley, P. W. and Bradshaw, G. L. Scientific discovery as problem-solving. Synthese 47, (1981 ), 1--27.
 
33
34
 
35
Sullivan, D., Koester, D., White, D. H. and Kern, R. Understanding Rapid Theoretical Change in Particle Physics: A Month-By-Month Co-Citation Analysis. Scientometrics, 2, 4 (1980), 309--319.
 
36
Tabah, A. N. Literature dynamics: studies on growth, diffusion, and epidemics. Annual Review of Information Science and Technology, 34, (1999), 249--286.
 
37
Tsymbal, A., Pechenizkiy, M., Cunningham, P. and Puuronen, S. Dynamic integration of classifiers for tracking concept drift in antibiotic resistance data Technical Report TCD-CS2005-26, Department of Computer Science, Trinity College, Dublin, Ireland, 2005.
 
38
Wasserman, S. and Faust, K. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.
 
39
 
40
York, D. G., Adelman, J., Anderson, J. E., Anderson, S. F., Annis, J., Bahcall, N. A. and al., e. The Sloan Digital Sky Survey: Technical summary. Astronomical Journal, 120, (2000), 1579--1587.
 
41

Collaborative Colleagues:
Chaomei Chen: colleagues
Jian Zhang: colleagues
Weizhong Zhu: colleagues
Michael Vogeley: colleagues