Skip to main content
SpringerLink
Log in

Design and implementation of an academic expert system through big data analysis

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Most researchers establish research directions in their study of new fields by providing expert advice or publishing expert papers. The existing academic search services display papers by field but do not provide experts by field. Therefore, researchers are left to judge experts in each field by analyzing the papers for themselves. In this paper, we design and implement an expert search system based on papers that have been published in the academic societies. The academic expert search system is based on a big data processing system to handle a large amount of data in academic fields. It calculates an expert score using quality and influence factors. The quality factor is calculated based on the citations, impact factor, and recentness of a paper. The influence factor is measured by the sparsity of a field and the degree of contributiveness of an author. The proposed system provides various services such as expert searches, keyword searches, the hot topics, expert relationships, and academic society statistics. By finding experts in a specific field, our system can support researchers’ research activities.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Google Scholar (2019) https://scholar.google.com. Accessed Dec 2019

  2. DBLP (2019) http://dblp.uni-trier.de. Accessed Dec 2019

  3. Research Gate (2019) https://www.researchgate.net. Accessed Dec 2019

  4. Academia Edu (2019) http://www.academia.edu. Accessed Dec 2019

  5. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci 102(46):16569–16572

    Article  Google Scholar 

  6. Egghe L (2006) Theory and practise of the g-index. Scientometrics 69(1):131–152

    Article  MathSciNet  Google Scholar 

  7. Machado JT, Lopes AM (2019) Ranking the scientific output of researchers in fractional calculus. Fract Calc Appl Anal 22(1):11–26

    Article  MathSciNet  Google Scholar 

  8. Gollapalli SD, Mitra P, Giles CL (2013) Ranking experts using author-document-topic graphs. In: Proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries, ACM, pp 87–96

  9. Guan Z, Miao G, McLoughlin R, Yan X, Cai D (2012) Co-occurrence-based diffusion for expert search on the web. IEEE Trans Knowl Data Eng 25(5):1001–1014

    Article  Google Scholar 

  10. Gollapalli SD, Mitra P, Giles CL (2012) Similar researcher search in academic environments. In: Proceedings of the 12th ACM/IEEE-CS joint conference on digital Libraries, ACM, pp 167–170

  11. Tran HD, Cabanac G, Hubert G (2017) Expert suggestion for conference program committees. In 2017 11th international conference on research challenges in information science (RCIS). IEEE, pp 221–232

  12. Sfyris GA, Fragkos N, Doulkeridis C (2016) Profile-based selection of expert groups. In: International conference on theory and practice of digital libraries. Springer, pp 81–93

  13. Yang L, Zhang W (2010) A study of the dependencies in expert finding. In: 2010 third international conference on knowledge discovery and data mining. IEEE, pp 355–358

  14. Zhu J, Huang X, Song D, Rüger S (2010) Integrating multiple document features in language models for expert finding. Knowl Inf Syst 23(1):29–54

    Article  Google Scholar 

  15. Daud A, Hussain S (2012) Publication venue based language modeling for expert finding. In: Proceedings of international conference on future communication and computer technology (ICFCCT 2012), pp 19–20

  16. Jiang X, Sun X, Yang Z, Zhuge H, Yao J (2016) Exploiting heterogeneous scientific literature networks to combat ranking bias: evidence from the computational linguistics area. J Assoc Inf Sci Technol 67(7):1679–1702

    Article  Google Scholar 

  17. Cifariello P, Ferragina P, Ponza M (2019) Wiser: a semantic approach for expert finding in academia based on entity linking. Inf Syst 82:1–16

    Article  Google Scholar 

  18. Li X, Watanabe T (2013) Automatic paper-to-reviewer assignment, based on the matching degree of the reviewers. Proc Comput Sci 22:633–642

    Article  Google Scholar 

  19. Liu D, Xu W, Du W, Wang F (2015) How to choose appropriate experts for peer review: an intelligent recommendation method in a big data context. Data Sci J 14:1–11

  20. Kale A, Kharat R, Bodkhe S, Apte P, Dhonde H (2015) Automated fair paper reviewer assignment for conference management system. In: 2015 international conference on computing communication control and automation. IEEE, pp 408–411

  21. Zhang J, Tang J, Li J (2007) Expert finding in a social network. In: International conference on database systems for advanced applications. Springer, pp 1066–1069

  22. Liao H, Xiao R, Cimini G, Medo M (2013) Ranking users, papers and authors in online scientific communities. arXiv:1311.3064

  23. Amjad T, Daud A, Akram A, Muhammed F (2016) Impact of mutual influence while ranking authors in a co-authorship network. Kuwait J Sci 43(3):101–109

    Google Scholar 

  24. Le LT, Shah C, Choi E (2016) Evaluating the quality of educational answers in community question-answering. In: Proceedings of the 16th ACM/IEEE-CS on joint conference on digital libraries. ACM, pp 129–138

  25. Pavan M, De Luca EW (2015) Semantic-based expert search in textbook research archives. In: SDA@ TPDL, pp 18–29

  26. Ollagnier A, Fournier S, Bellot P (2018) BIBLME RecSys: harnessing bibliometric measures for a scholarly paper recommender system. In: BIR 2018 workshop on bibliometric-enhanced information retrieval

  27. Liu L, Zuo W, Han J, Peng T (2018) DEDSC: a domain expert discovery method based on structure and content. Int J Uncertain Fuzziness Knowl-Based Syst 26(04):583–599

    Article  Google Scholar 

  28. Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. HotCloud 10(10–10):95

    Google Scholar 

  29. Khan S, Liu X, Shakil KA, Alam M (2017) A survey on scholarly data: from big data perspective. Inf Process Manag 53(4):923–944

    Article  Google Scholar 

  30. Khan S, Shakil KA, Alam M (2016) Cloud-based big data management and analytics for scholarly resources: current trends, challenges and scope for future research. arXiv:1606.01808

  31. Simović A (2018) A big data smart library recommender system for an educational institution. Lib Hi Tech 36(3):498–523

    Article  Google Scholar 

  32. Xu Z (2017) The analytics and applications on supporting big data framework in wireless surveillance networks. Int J Soc Humanist Comput 2(3–4):141–149

    Article  Google Scholar 

  33. Robertson SE (1977) The probability ranking principle in IR. J Doc 33(4):294–304

    Article  Google Scholar 

  34. High Charts (2019) http://www.highcharts.com. Accessed Dec 2019

  35. Shvachko K, Kuang H, Radia S, Chansler R (2010) The hadoop distributed file system. In: MSST pp 1–10

  36. HBase (2019) http://hbase.apache.org. Accessed Dec 2019

  37. PostgreSQL (2019) https://www.postgresql.org. Accessed Dec 2019

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (No. 2019R1A2C2084257, NRF-2017S1A5B8059946), by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. B0101-15-0266, Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis), by the AURI (Korea Association of University, Research institute and Industry) grant funded by the Korea Government (MSS : Ministry of SMEs and Startups). (No. S2929950, HRD program for 2020), by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk, 28655, Korea

    Dojin Choi, Hyeonbyeong Lee & Jaesoo Yoo

  2. Department of Software Convergence, Wonkwang University, Iksan, Korea

    Kyoungsoo Bok

Authors
  1. Dojin Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyeonbyeong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kyoungsoo Bok
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jaesoo Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaesoo Yoo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choi, D., Lee, H., Bok, K. et al. Design and implementation of an academic expert system through big data analysis. J Supercomput 77, 7854–7878 (2021). https://doi.org/10.1007/s11227-020-03446-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03446-0

Keywords

Search

Navigation