An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science.

Autor/inn/en	Dong, Kun; Xu, Haiyun; Luo, Rui; Wei, Ling; Fang, Shu
Titel	An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science.
Quelle	In: Scientometrics, (2018) 2, S.849-868 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-018-2694-x
Schlagwörter	Interdisciplinary topic; Topic identification; Integrated method; Information science and library science
Abstract	Abstract Given that many frontiers and hotspots of science and technology are emerging from interdisciplines, the accurate identification and forecasting of interdisciplinary topics has become increasingly significant. Existing methods of interdisciplinary topic identification have their respective application fields, and each identification result can help researchers acquire partial characteristics of interdisciplinary topics. This paper offers an integrated method for identifying and predicting interdisciplinary topics from scientific literature. It integrates various methods, including co-occurrence networks analysis, high-TI terms analysis and burst detection, and offers an overall perspective into interdisciplinary topic identification. The results of the different methods are mutually confirmed and complemented, further overviewing the characteristics of the interdisciplinary field and highlighting the importance or potential of interdisciplinary topics. In this study, Information Science and Library Science is selected as a case study. The research has clearly shown that more accurate and comprehensive results can be achieved for interdisciplinary topic identification and prediction by employing this integrated method. Further, the integration of different methods has promising potential for application in knowledge discovery and scientific measurement in the future.
Erfasst von	OLC
Update	2023/2/05