Integrating researchers’ scientific production information through Ogmios


Nowadays, many R&I institutions are presently implementing mechanisms to measure and rate their scientific production so as to comply with current legislation and to support research management and decision making. In many cases, they rely on the implementation of current research information systems (CRIS). This is a challenging task that often requires major human intervention and supervision to manually include all scientific production, projects, patents, etc., in the system. In this paper, we present Ogmios, a system that aims to reduce the time and effort of this process. Ogmios is a CRIS that automatically extracts and combines information from different sources, such as academic social networks or academic search engines. This redundancy helps to reduce potential errors. Additionally, Ogmios relies on other sources, such as online subscription-based scientific citation indexing services, to add metadata to information collected for ranking purposes. We have assessed the performance of this system with a sample of 216 researchers from the University of Málaga; 815 profiles were retrieved and validated with an accuracy of over 90% in profile detection. The main contribution of this work is Ogmios’s autonomous capacity to retrieve and combine all necessary information on scientific profiles and production from different data sources and, also, its adaptability to any university or research institution.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.


  1. 1.

    Green JT, McArdle IJ, Rutherford SL, Turner T, van Baren J, Fowler N et al (2014) Developing tools to inform the management of research and translating existing good practice. Technical Report. Imperial College. London.

  2. 2.

    Brown S (2009) A comparative review of research assessment regimes in five countries and the role of libraries in the research assessment process. OCLC Research

  3. 3.

    Galloway ID (1990) Strategic management in public sector research organisations: a critical review. Int J Public Sect Manag 3(1).

  4. 4.

    Jeffery K, Asserson A (2009) Institutional repositories and current research information systems. N Rev Inf Netw 14(2):71–83

    Article  Google Scholar 

  5. 5.

    Dijk E, Baars C, Hogenaar A, van Meel M (2006) NARCIS: the gateway to dutch scientific information. In: ELPUB, pp 49–58

  6. 6.

    Davis PM, Connolly MJ (2007) Institutional repositories: evaluating the reasons for non-use of Cornell University’s installation of DSpace. D-Lib Magazine, vol 13, n 3/4.

  7. 7.

    Van Westrienen G, Lynch CA (2005) Academic institutional repositories: deployment status in 13 nations as of mid 2005 D-Lib. Magazine 11(9)

  8. 8.

    Ribeiro L, de Castro P, Mennielli M (2016) Final report: EUNIS-EUROCRIS Joint Survey on CRIS and IR. An ERAI (Eunis Research and Analysis Initiative) Report. March 2016

  9. 9.

    Nabavi M, Jeffery K, Jamali HR (2016) Added value in the context of research information systems. Program 50(3):325–339

    Article  Google Scholar 

  10. 10.

    Moreira JM, Laranjeira C, Carvalho J, Ribeiro F, Lopes P, Graça P (2017) Integrating a national network of institutional repositories into the national/international research management ecosystem. Procedia Comput Sci 106:146–152

    Article  Google Scholar 

  11. 11.

    Rybinski H, Skonieczny L, Koperwas J, Struk W, Stepniak J, Kubrak W (2017) Integrating IR with CRIS–a novel researcher-centric approach. Program 51(3):298–321

    Article  Google Scholar 

  12. 12.

    Hornbostel S (2006) From CRIS to CRIS: integration and interoperability. Leuven University Press, pp 29–38

  13. 13.

    Schirrwagen J, Jahn N (2013) Research in context (in view of recent results from OpenAire Plus and from the library perspective). The 6th year of the seminar focused on storage and providing access to the grey literature, National Library of Technology, Praha

  14. 14.

    Van Noorden R (2014) Online collaboration: scientists and the social network. Nature 512(7513):126–129

    Article  Google Scholar 

  15. 15.

    Azoulay P, Stellman A, Zivin JG (2006) PublicationHarvester: an open-source software tool for science policy research. Res Policy 35(7):970–974

    Article  Google Scholar 

  16. 16.

    Ferrara E, De Meo P, Fiumara G, Baumgartner R (2014) Web data extraction, applications and techniques: a survey. Knowl-Based Syst 70:301–323

    Article  Google Scholar 

  17. 17.

    Yang JM, Cai R, Wang Y, Zhu J, Zhang L, Ma WY (2009) Incorporating site-level knowledge to extract structured data from web forums. In: Proceedings of the 18th international conference on World wide web. ACM, pp 181–190

  18. 18.

    Noureddine H, Jarkass I, Hazimeh H, Khaled OA, Mugellini E (2015) CARP: correlation based approach for researcher profiling. In: SEKE, pp 461–464

  19. 19.

    Geuna A, Kataishi R, Toselli M, Guzmán E, Lawson C, Fernandez-Zubieta A, Barros B (2015) SiSOB data extraction and codification: a tool to analyze scientific careers. Res Policy 44(9):1645–1658

    Article  Google Scholar 

  20. 20.

    Harzing AWK, Van der Wal R (2008) Google Scholar as a new source for citation analysis. Ethics Sci Environ Polit 8(1):61–73

    Article  Google Scholar 

  21. 21.

    Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 990–998

  22. 22.

    Liu J, Liu D, Yan X, Dong L, Zeng T, Zhang Y, Tang J (2014) AMiner-mini: a people search engine for University. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 2069–2071

  23. 23.

    Tang J, Fong AC, Wang B, Zhang J (2012) A unified probabilistic framework for name disambiguation in digital library. IEEE Trans Knowl Data Eng 24(6):975–987

    Article  Google Scholar 

  24. 24.

    Wang C (2012) AMBER: turning annotations into knowledge. In: Proceedings of the 21st international conference on World Wide Web. ACM, pp 191–196

  25. 25.

    Han H, Giles L, Zha H, Li C, Tsioutsiouliklis K (2004). Two supervised learning approaches for name disambiguation in author citations. In Digital Libraries, 2004. Proceedings of the 2004 joint ACM/IEEE conference on. IEEE, pp 296–305

  26. 26.

    Smalheiser NR, Torvik VI (2009) Author name disambiguation. Ann Rev Inf Sci Technol 43(1):1–43

    Article  Google Scholar 

  27. 27.

    Ferreira AA, Gonçalves MA, Laender AH (2012) A brief survey of automatic methods for author name disambiguation. Acm Sigmod Record 41(2):15–26

    Article  Google Scholar 

  28. 28.

    Giles CL, Zha H, Han H (2005) Name disambiguation in author citations using a k-way spectral clustering method. In: Digital Libraries, 2005. JCDL’05. Proceedings of the 5th ACM/IEEE-CS joint conference on. IEEE, pp. 334–343

  29. 29.

    Pereira DA, Ribeiro-Neto B, Ziviani N, Laender AH, Gonçalves MA, Ferreira AA (2009). Using web information for author name disambiguation. In: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries. ACM, pp 49–58

  30. 30.

    Orduña-Malea E, Ayllón JM, Martín-Martín A, López-Cózar ED (2014) About the size of Google Scholar: playing the numbers. arXiv preprint arXiv:1407.6239

  31. 31.

    Satariano A (2016) Bill Gates-backed research network targets advertising revenue, 2016. Accessed 24 June 2018

  32. 32. ORCID. Accessed 24 June 2018

  33. 33. Content—Scopus—Solutions—Elsevier. Accessed 20 Dec 2017

  34. 34. Clarivate analytics—web of science. Accessed 24 June 2018

  35. 35.

    Martin-Martin A, Orduña-Malea E, Harzing AW, López-Cózar ED (2017) Can we use Google Scholar to identify highly-cited documents? J Informetr 11(1):152–163

    Article  Google Scholar 

  36. 36.

    D’Angelo CA, Giuffrida C, Abramo G (2011) A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments. J Assoc Inf Sci Technol 62(2):257–269

    Article  Google Scholar 

  37. 37.

    Abdulhayoglu MA, Thijs B (2017) Use of ResearchGate and Google CSE for author name disambiguation. Scientometrics 111(3):1965–1985

    Article  Google Scholar 

Download references


Ogmios is a project supported and funded by the Vice-Rectorate for Research and Knowledge Transfer of the University of Málaga, Spain.

Author information



Corresponding author

Correspondence to Eduardo Guzmán.

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

Verify currency and authenticity via CrossMark

Cite this article

Verdugo, N., Guzmán, E. & Urdiales, C. Integrating researchers’ scientific production information through Ogmios. Knowl Inf Syst (2020).

Download citation


  • Research information system
  • Data extraction
  • Knowledge engineering
  • Author disambiguation
  • Research of research