Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset

Scientometrics

Volumn 111, Issue 2, 2017, Pages 1071-1087

  Glänzel, Wolfgang ^a,b   Thijs, Bart ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

Astronomy; Astrophysics; Bibliographic coupling; Clustering; Core documents; Hybrid clustering; NLP

Indexed keywords

EID: 85013392644     PISSN: 01389130     EISSN: 15882861     Source Type: Journal    
DOI: 10.1007/s11192-017-2301-6     Document Type: Article

References (24)

1. Ahlgren, P., & Colliander, C. (2009). Document–document similarity approaches and science mapping: experimental comparison of five approaches. Journal of Informetrics, 3(1), 49–63.
2. Batagelj, V., & Mrvar, A. (2003). Pajek-analysis and visualization of large networks. In M. Jünger & P. Mutzel (Eds.), Graph drawing software (pp. 77–103). Berlin: Springer.
3. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, P10008. doi:10.1088/1742-5468/2008/10/P10008.
4. Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404.
5. Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22(1), 155–205.
6. Garfield, E. (1969). Permuterm Subject Index—The primordial dictionary of science. Current Contents, 12(22), 4.
7. Glänzel, W. (2012). The role of core documents in bibliometric network analysis and their relation with h-type indices. Scientometrics, 93(1), 113–123.
8. Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.
9. Glänzel, W., & Thijs, B. (2011). Using ‘core documents’ for the representation of clusters and topics. Scientometrics, 88(1), 297–309.
10. Glänzel, W., & Thijs, B. (2012a). Hybrid solutions—The best of all possible worlds? Bibliometrie & Praxis und Forschung, 1(3). doi:10.5283/bpf.156.
11. Glänzel, W., & Thijs, B. (2012b). Using ‘core documents’ for detecting and labelling new emerging topics. Scientometrics, 91(2), 399–416.
12. Glänzel, W., & Thijs, B. (2015). Using hybrid methods and ‘core documents’ for the representation of clusters and topics. The astronomy dataset. In A. A. Salah, Y. Tonta, A. A. Salah, C. Sugimoto, & U. Al (Eds.), Proceedings of ISSI 2015—The 15th international conference on scientometrics and informetrics (pp. 1085–1090). Istanbul: Turkey.
13. Glenisson, P., Glänzel, W., Janssens, F., & de Moor, B. (2005). Combining full text and bibliometric information in mapping scientific disciplines. Information Processing and Management, 41(6), 1548–1572.
14. Gould, R. V., & Fernandez, R. M. (1989). Structures of mediation: a formal approach to brokerage in transaction networks. Sociological Methodology, 19, 89–126.
15. Hicks, D. (1987). Limitations of co-citation analysis as a tool for science policy. Social Studies of Science, 17(2), 295–316.
16. Janssens, F. (2007). Clustering of scientific fields by integrating text mining and bibliometrics. Ph.D. Thesis, Faculty of Engineering, Katholieke Universiteit Leuven, Belgium. http://www.hdl.handle.net/1979/847.
17. Janssens, F., Glänzel, W., & de Moor, B. (2008). A hybrid mapping of information science. Scientometrics, 75(3), 607–631.
18. Klein, D., & Manning, Ch. D. (2003). Accurate unlexicalized parsing. In Proceedings of the 41st annual meeting of the association for computational linguistics (pp. 423–430).
19. Kostoff, R. N., Eberhart, H. J., & Toothman, D. R. (1997). Database tomography for information retrieval. Journal of Information Science, 23(4), 301–311.
20. Thijs, B., Glänzel, W., & Meyer, M. (2015). Using noun phrases extraction for the improvement of hybrid clustering with text- and citation-based components. The example of Information system research. In Proceedings of the workshop mining scientific papers: Computational linguistics and bibliometrics (Vol. 1384). International Society of Scientometrics and Informetrics Conference (ISSI). Istanbul (Turkey), 29 June 2015. http://ceur-ws.org/Vol-1384/.
21. Thijs, B., Schiebel, E., & Glänzel, W. (2013). Do second-order similarities provide added-value in a hybrid approach? Scientometrics, 96(3), 667–677.
22. Todorov, R. (1992). Displaying content of scientific journals: A co-heading analysis. Scientometrics, 23(2), 319–334.
23. Turner, W. A., Chartron, G., Laville, F., & Michelet, M. (1988). Packaging information for peer review: new co-word analysis techniques. In A. van Raan (Ed.), Handbook of quantitative studies of science and technology. North Holland: Elsevier.
24. Zitt, M., & Basseacoulard, E. (1996). Reassessment of co-citation methods for science indicators: Effect of methods improving recall rates. Scientometrics, 37(2), 223–244.