Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Exploratory Search for Scientific Articles

  • 44 Accesses


It is intuitively clear that search for scientific publications often has many characteristics of exploratory search. The purpose of this paper is to formalize this intuitive understanding, explore which scientific search tasks can be classified as research search ones, what general approaches to the research search problem exist, and how they are implemented in specialized search engines for scientists. We overview the existing works that address the information-seeking behavior of scientists and a special variant of search called exploratory search. There are several types of search behavior typical for scientists; we show that most of them are exploratory ones. Exploratory search differs from information retrieval and requires special support from the search systems. We analyze seventeen search systems for academicians (from Google Scholar, Scopus, and Web of Science to ResearchGate) from the perspective of exploratory search support. We find that most of them do not go far beyond simple information retrieval, and there is room for further improvements, especially in collaborative search support.

This is a preview of subscription content, log in to check access.


  1. 1.



  1. 1

    Marchionini, G., Exploratory search: From finding to understanding, Commun. ACM, 2006, vol. 49, no. 4, pp. 41–46.

  2. 2

    White, R.W. and Roth, R.A., Exploratory Search: Beyond the Query-Response Paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services, Morgan and Claypool, 2009.

  3. 3

    Marchionini, G., Information Seeking in Electronic Environments, Cambridge University Press, 1997.

  4. 4

    Ellis, D., Cox, D., and Hall, K., A comparison of the information seeking patterns of researchers in the physical and social sciences, J. Doc., 1993, vol. 49, no. 4, pp. 356–369.

  5. 5

    Ellis, D., A behavioural approach to information retrieval system design, J. Doc., 1989, vol. 45, no. 3, pp. 171–212.

  6. 6

    Niu, X., Hemminger, B.M., Lown, C., Adams, S., Brown, C., Level, A., McLure, M., Powers, A., Tennant, M.R., and Cataldo, T., National study of information seeking behavior of academic researchers in the United States, J. Assoc. Inf. Sci. Technol., 2010, vol. 61, no. 5, pp. 869–890.

  7. 7

    Niu, X. and Hemminger, B.M., A study of factors that affect the information-seeking behavior of academics scientists, J. Am. Soc. Inf. Sci. Technol., 2012, vol. 63, no. 2, pp. 336–353.

  8. 8

    Meho, L.I. and Tibbo, H.R., Modeling the information-seeking behavior of social scientists: Ellis’s study revisited, J. Assoc. Inf. Sci. Technol., 2003, vol. 54, no. 6, pp. 570–587.

  9. 9

    Palagi, E., Gandon, F., Giboin, A., and Troncy, R., A survey of definitions and models of exploratory search, Proc. ACM Workshop Exploratory Search and Interactive Data Analytics, 2017, pp. 3–8.

  10. 10

    Athukorala, K., Hoggan, E., Lehtio, A., Ruotsalo, T., and Jacucci, G., Information-seeking behaviors of computer scientists: Challenges for electronic literature search tools, Proc. 76th Annu. Meet. Association for Information Science and Technology (ASIS&T), 2013, vol. 50, pp. 1–11.

  11. 11

    Jiang, T., Exploratory search: A critical analysis of the theoretical foundations, system features, and research trends, Library and Information Sciences, Springer, 2014, pp. 79–103.

  12. 12

    Yanina, A.O. and Vorontsov, K.V., Multimodal thematic models for exploratory search in a collective blog, Mashinnoe Obuchenie Anal. Dannykh, 2016, vol. 2, no. 2, pp. 173–186.

  13. 13

    Beel, J., Gipp, B., Langer, S., and Breitinger, C., Research-paper recommender systems: A literature survey, Int. J. Digital Libr., 2016, vol. 17, no. 4, pp. 305–338.

  14. 14

    Parkhomenko, P.A., Grigor’ev, A.A., and Astrakhantsev, N.A., Overview and experimental comparison of text clustering methods, Tr. Inst. Sistemnogo Program. Ross. Akad. Nauk, 2017, vol. 29, no. 2, pp. 161–200.

  15. 15

    Tian, H. and Zhuo, H.H., Paper2vec: Citation-context based document distributed representation for scholar recommendation, 2017.

  16. 16

    Kong, X., Mao, M., Wang, W., Liu, J., and Xu, B., Voprec: Vector representation learning of papers with text information and structural identity for recommendation, IEEE Trans. Emerging Top. Comput., 2018.

  17. 17

    Morris, M.R. and Horvitz, E., SearchTogether: An interface for collaborative web search, Proc. 20th Annu. ACM Symp. User Interface Software and Technology, 2007, pp. 3–12.

  18. 18

    Morris, M.R., Collaborative search revisited, Proc. Conf. Computer Supported Cooperative Work, 2013, pp. 1181–1192.

  19. 19

    Chen, C., Ibekwe-SanJuan, F., and Hou, J., The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis, J. Am. Soc. Inf. Sci. Technol., 2010, vol. 61, no. 7, pp. 1386–1409.

  20. 20

    McCay-Peet, L., Quan-Haase, A., and Kern, D., Exploratory search in digital libraries: A preliminary examination of the use and role of interface features, Proc. 78th Annu. Meet. Association for Information Science and Technology, 2015, vol. 52, pp. 1–4.

  21. 21

    Li, H., Councill, I., Lee, W.-C., and Giles, C.L., CiteSeerZ: An architecture and web service design for an academic document search engine, Proc. 15th Int. Conf. World Wide Web, 2006, pp. 883–884.

  22. 22

    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z., ArnetMiner: Extraction and mining of academic social networks, Proc. 14th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2008, pp. 990–998.

  23. 23

    Ammar, W., Groeneveld, D., Bhagavatula, C., et al., Construction of the literature graph in semantic scholar, 2018.

  24. 24

    Zaugg, H., West, R.E., Tateishi, I., and Randall, D.L., Mendeley: Creating communities of scholarly inquiry through research collaboration, TechTrends, 2011, vol. 55, no. 1, pp. 32–36.

  25. 25

    Astrakhantsev, N.A., Fedorenko, D.G., and Turdakov, D.Yu., Methods for automatic term recognition in domain-specific text collections: A survey, Program. Comput. Software, 2015, vol. 41, no. 6, pp. 336–349.

  26. 26

    Varlamov, M.I. and Turdakov, D.Yu., A survey of methods for the extraction of information from Web resources, Program. Comput. Software, 2016, vol. 42, no. 5, pp. 279–291.

  27. 27

    Yatskov, A.K., Varlamov, M.I., and Turdakov, D.Yu., Extraction of data from mass media web sites, Program. Comput. Software, 2018, vol. 44, no. 5, pp. 344–352.

  28. 28

    Sysoev, A.A., Andrianov, I.A., and Khadzhiiskaia, A.Y., Coreference resolution in Russian: State-of-the-art approaches application and evolvement, Proc. Int. Conf. Computational Linguistics and Intellectual Technologies Dialog, 2017, pp. 327–347.

Download references


This work was supported by the Russian Foundation for Basic Research, project no. 17-07-00978 A.

Author information

Correspondence to Y. R. Nedumov or S. D. Kuznetsov.

Additional information

Translated by Yu. Kornienko

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nedumov, Y.R., Kuznetsov, S.D. Exploratory Search for Scientific Articles. Program Comput Soft 45, 405–416 (2019). https://doi.org/10.1134/S0361768819070089

Download citation