, Volume 115, Issue 2, pp 849–868 | Cite as

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

  • Kun Dong
  • Haiyun Xu
  • Rui Luo
  • Ling Wei
  • Shu Fang


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.


Interdisciplinary topic Topic identification Integrated method Information science and library science 



This study was supported by the National Natural Science Foundation of China (Grant No. 71704170), the China Postdoctoral Science Foundation funded Project (2016M590124), the Youth Innovation Fund of Promotion Association, CAS (2016159) and Informationization Initiative of Chinese Academy of Sciences (XXH13506-203).


  1. Breunig, M. M. (2000). LOF: Identifying density-based local outliers. In ACM SIGMOD international conference on management of data, May 16–18, 2000, Dallas, TX, USA (Vol. 29, pp. 93–104). DBLP.Google Scholar
  2. 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 chemsitry. Scientometrics, 22(1), 155–205.CrossRefGoogle Scholar
  3. Callon, M., Courtial, J. J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks—An introduction to co-word analysis. Social Science Information, 22(2), 191–235.CrossRefGoogle Scholar
  4. Chawla, S., & Sun, P. (2006). Slom: A new measure for local spatial outliers. Knowledge and Information Systems, 9(4), 412–429.CrossRefGoogle Scholar
  5. Chu, J., & Qian, Q. (2014). Analysis of research focus and research methods in the field of knowledge management during the past decade. Information Science, 10, 156–160.Google Scholar
  6. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.CrossRefGoogle Scholar
  7. Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30(2), 119–145.CrossRefGoogle Scholar
  8. Hawkins, D. M. (1980). Identification of outliers. Biometrics, 37(4), 860.zbMATHGoogle Scholar
  9. Hong, S., Lin, J., & Zhang, Y. (2015). Density-based outlier detection on uncertain data. Computer Science, 42(5), 230–233.Google Scholar
  10. Kleinberg, J. (2003). Bursty and hierarchical structure in streams. In Eighth ACM SIGKDD international conference on knowledge discovery and data mining (Vol. 7, pp. 91–101). ACM.Google Scholar
  11. Li, X. (2017). Research hotspots and trend analysis of railway transportation engineering management based on knowledge graph. Rail Way Transport and Economy, 1, 81–87.Google Scholar
  12. Li, C., Liu, F., & Guo, F. (2013). Analysis on interdisciplinary research topics with cinder of overlapping communities visualization software: Taking the information science and computer science for example. Library and Information Service, 7, 75–80.Google Scholar
  13. Mane, K. K., & Börner, K. (2004). Mapping topics and topic bursts in pnas. Proceedings of the National Academy of Sciences, 101(Suppl 1), 5287–5290.CrossRefGoogle Scholar
  14. Min, C., & Sun, J. (2014). Clustering analysis on discipline-crossing research hotspots: An example of library and information science and journalism and communication studies. Library and Information Service, 58(1), 109–116.Google Scholar
  15. Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2003, March). LOCI: Fast outlier detection using the local correlation integral. In: Data Engineering, 2003. Proceedings. 19th International Conference on (pp. 315–326). IEEE.Google Scholar
  16. Rokaya, M., Atlam, E., Fuketa, M., Dorji, T. C., & Aoe, J. I. (2008). Ranking of field association terms using co-word analysis. Information Processing and Management, 44(2), 738–755.CrossRefGoogle Scholar
  17. Sci2Team. (2009). Science of science (Sci2) tool. Retrieved July 11, 2015 from
  18. Tamura, S., Tamura, K., Kitakami, H., & Hirahara, K. (2012). Clustering-based burst-detection algorithm for web-image document stream on social media. In IEEE international conference on systems, man, and cybernetics (Vol. 2, pp. 703–708). IEEE.Google Scholar
  19. TDA. (2013). Thomson data analyzer. Retrieved July 14, 2016 from
  20. Trotta, D., & Garengo, P. (2017). A co-word analysis on human resource management literature: The role of technological innovation from 2007–2017. In 20th Excellence in services international conference conference proceedings (Vol. 9, pp. 797–810). ISBN.Google Scholar
  21. Wang, L. (2012). On the topic evolution based on outlier data. Doctoral dissertation, Graduate University of Chinese Academy of Sciences.Google Scholar
  22. Wei, L., Xu, H., Guo, T., et al. (2015). Study on the interisciplinary topics of information science based on weak co-occurrence and burst detecting. Library and Information Service, 59(21), 105–114.Google Scholar
  23. Xu, H., Guo, T., Yue, Z., Ru, L., & Fang, S. (2016). Interdisciplinary topics of information science: A study based on the terms interdisciplinarity index series. Scientometrics, 106(2), 1–19.CrossRefGoogle Scholar
  24. Xu, H. Y., Yue, Z. H., Wang, C., Dong, K., Pang, H. S., & Han, Z. (2017). Multi-source data fusion study in scientometrics. Scientometrics, 111(2), 773–792.CrossRefGoogle Scholar
  25. Zhou, S. (2015). Burst information monitoring research on the songs of the South based on Kleinberg algorithm. Computer Knowledge and Technology, 2, 86–89.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.Chengdu Documentation and Information CenterChinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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