Journal of Intelligent Information Systems

, Volume 37, Issue 1, pp 65–88 | Cite as

Research interests: their dynamics, structures and applications in unifying search and reasoning

  • Yi Zeng
  • Erzhong Zhou
  • Yan Wang
  • Xu Ren
  • Yulin Qin
  • Zhisheng Huang
  • Ning Zhong


Most scientific publication information, which may reflects scientists’ research interests, is publicly available on the Web. Understanding the characteristics of research interests from previous publications may help to provide better services for scientists in the Web age. In this paper, we introduce some parameters to track the evolution process of research interests, we analyze their structural and dynamic characteristics. According to the observed characteristics of research interests, under the framework of unifying search and reasoning (ReaSearch), we propose interests-based unification of search and reasoning (I-ReaSearch). Under the proposed I-ReaSearch method, we illustrate how research interests can be used to improve literature search on the Web. According to the relationship between an author’s own interests and his/her co-authors interests, social group interests are also used to refine the literature search process. Evaluation from both the user satisfaction and the scalability point of view show that the proposed I-ReaSearch method provides a user centered and practical way to problem solving on the Web. The efforts provide some hints and various methods to support personalized search, and can be considered as a step forward user centric knowledge retrieval on the Web. From the standpoint of the Active Media Technology (AMT) on the Wisdom Web, in this paper, the study on the characteristics of research interests is based on complex networks and human dynamics, which can be considered as an effort towards utilizing information physics to discover and explain the phenomena related to research interests of scientists. The application of research interests aims at providing scientific researchers best means and best ends in an active way for literature search on the Web.


Research interest detection Retained interest Interest duration Web search refinement Unifying search and reasoning 



This study is supported by the European Commission under the 7th framework programme, Large Knowledge Collider (FP7-215535). The author would like to thank Yiyu Yao for his constructive discussion on user interests based knowledge retrieval, Rui Guo and Chao Gao on their useful comments on network theory which are used for interpreting the phenomenon observed in this study, Jian Yang for his suggestions on measurement of research interests.


  1. Aleman-Meza, B., Hakimpour, F., Arpinar, I. B., & Sheth, A. P. (2007). Swetodblp ontology of computer science publications. Journal of Web Semantics, 5(3):151–155.CrossRefGoogle Scholar
  2. Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2(6), 396–408.CrossRefGoogle Scholar
  3. Barabási, A. L. (2002). Linked: How everything is connected to everything else and what it means for science, business and everyday life (1 ed.). Perseus Publishing.Google Scholar
  4. Barabási, A. L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435, 207–211.CrossRefGoogle Scholar
  5. Berners-Lee, T., & Fischetti, M. (1999). Weaving the web: The original design and ultimate destiny of the world wide web by its inventor. HarperSanFrancisco.Google Scholar
  6. Braam, R. R., Moed, H. F., & Raan, A. F. J. v. (1991). Mapping of science by combined co-citation and word analysis: II. Dynamical aspects. Journal of the American Society for Information Science, 42(4), 252–266.CrossRefGoogle Scholar
  7. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. National Academy Press.Google Scholar
  8. Chen, C. M. (2006). Citespace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.CrossRefGoogle Scholar
  9. Dezso, Z., Almaas, E., Lukács, A., Rácz, B., Szakadát, I., & Barabási, A. L. (2006). Dynamics of information access on the web. Physical Review E, 73(066132), 1–6.Google Scholar
  10. Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology hermann ebbinghaus. Teachers College, Columbia University.Google Scholar
  11. Erten, C., Harding, P. J., Kobourov, S. G., Wampler, K., & Yee, G. (2004). Exploring the computing literature using temporal graph visualization. In Proceedings of the 2004 SPIE Conference on visualization and data analysis (Vol. 5295, pp. 45–56).Google Scholar
  12. Fensel, D., & van Harmelen, F. (2007). Unifying reasoning and search to web scale. IEEE Internet Computing, 11(2), 96, 94–95.CrossRefGoogle Scholar
  13. Greene, J. H. (1997). Production and inventory control handbook (3 ed.). New York: McGraw-Hill.Google Scholar
  14. Haight, F. A. (1967). Handbook of the Poisson distribution. New York: Wiley.MATHGoogle Scholar
  15. Han, X. P., Zhou, T., & Wang, B. H. (2008). Modeling human dynamics with adaptive interest. New Journal of Physics, 10(073010), 1–8.Google Scholar
  16. Hoeber, O. (2008). Web information retrieval support systems: The future of web search. In Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (Vol. 3, pp. 29–32).Google Scholar
  17. Liu, J. M. (2006). Active media technologies (amt) from the standpoint of the wisdom web. In Y. Li, M. Looi, & N. Zhong (Eds.), Proceedings of the 4th international conference on active media technology (AMT 2006). Frontiers in artificial intelligence and applications (Vol. 138, pp. 3–6). IOS Press.Google Scholar
  18. Masoliver, J., Montero, M., & Weiss, G. H. (2003). Continuous-time random-walk model for financial distributions. Physical Review E, 67(021112), 1–10.Google Scholar
  19. Oliveira, J. G., & Barabási, A. L. (2005). Darwin and Einstein correspondence patterns. Nature, 437, 1251.CrossRefGoogle Scholar
  20. Popescul, A., Flake, G. W., Lawrence, S., Ungar, L. H., & Giles, C. L. (2000). Clustering and identifying temporal trends in document databases. In Proceedings of the 2000 IEEE advances in digital libraries (pp. 173–182).Google Scholar
  21. Qiu, F., & Cho, J. (2006). Automatic identification of user interest for personalized search. In Proceedings of the 2006 international world wide web conference.Google Scholar
  22. Reynolds, P. (2003). Call center staffing. Lebanon, Tennessee: The Call Center School Press.Google Scholar
  23. Roy, S., Gevry, D., & Pottenger, W. M. (2002). Methodologies for trend detection in textual data mining. In Proceedings of the 2002 text mining workshop at the second SIAM conference on data mining.Google Scholar
  24. Shneiderman, B. (2008). Science 2.0. Science, 319, 1349–1350.CrossRefGoogle Scholar
  25. Small, H. G., & Griffith, B. C. (1974). The structure of scientific literatures: I. Identifying and graphing specialties. Science Studies, 4, 17–40.CrossRefGoogle Scholar
  26. Yao, Y. Y. (2002). Information retrieval support systems. In Proceedings of the 2002 IEEE international conference on fuzzy systems (pp. 773–778).Google Scholar
  27. Yao, J. T., & Yao, Y. Y. (2003). Web-based support systems (reprint from wss’03). In Proceedings of the 2004 international workshop on web-based support systems (pp. 1–5).Google Scholar
  28. Yao, Y. Y., Zeng, Y., Zhong, N., & Huang, X.J. (2007). Knowledge retrieval (kr). In Proceedings of the 2007 IEEE/WIC/ACM international conference on web intelligence (pp. 729–735).Google Scholar
  29. Zeng, Y., Ren, X., Qin, Y. L., Zhong, N., Huang, Z. S., & Wang, Y. (2009). Social relation based scalable semantic search refinement. In The 1st asian workshop on scalable semantic data processing (AS2DP).Google Scholar
  30. Zeng, Y., Yao, Y. Y., Zhong, N. (2009b). Dblp-sse: A dblp search support engine. In Proceedings of the 2009 IEEE/WIC/ACM international conference on web intelligence (pp. 626–630).Google Scholar
  31. Zeng, Y., Wang, Y., Huang, Z. S., Damljanovic, D., Zhong, N., et al. (2010a) User interests: Definition, vocabulary, and utilization in unifying search and reasoning. In Proceedings of the 2010 international conference on active media technology (pp. 98–107). Springer.Google Scholar
  32. Zeng, Y., Zhong, N., Wang, Y., Qin, Y. L., Huang, Z. S., Zhou, H. Y., et al. (2010b). User-centric query refinement and processing using granularity based strategies. Knowledge and Information Systems. doi: 10.1007/s10115-010-0298-8.Google Scholar
  33. Zeng, Y., Zhou, E. Z., Qin, Y. L., & Zhong, N. (2010b). Research interests: Their dynamics, structures and applications in web search refinement. In Proceedings of the 2010 IEEE/WIC/ACM international conference on web intelligence (pp. 639–646). IEEE Computer Society.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Yi Zeng
    • 1
  • Erzhong Zhou
    • 1
  • Yan Wang
    • 1
  • Xu Ren
    • 1
  • Yulin Qin
    • 1
  • Zhisheng Huang
    • 2
  • Ning Zhong
    • 3
  1. 1.International WIC InstituteBeijing University of TechnologyBeijingPeople’s Republic of China
  2. 2.Division of Mathematics and Computer ScienceVrije University AmsterdamAmsterdamthe Netherlands
  3. 3.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashi-CityJapan

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