Monitoring the Development of University Scientific Schools in University Knowledge Management

Gulnaz Zhomartkyzy, Tatyana Balova

2015

Abstract

This paper proposes a technological approach to university scientific knowledge management which integrates the ontology based knowledge model and the methods of university scientific resource intellectual processing. The process-oriented On-To-Knowledge methodology is used as the basis for university scientific knowledge management. Some models and methods of university scientific knowledge management have been studied. The developed model of a specialist that reflects the level of scientific activity productivity and overall assessment of the employee's scientific activity has been described. A specialist’s competence in knowledge areas is based on the processing of information resources. The approach to the university scientific school identification based on the clustering of university academic community common interests has been described.

References

  1. Klimov, S. M., 2002. Intellectual resources of society. SPb: IFEREL, Znanie.
  2. Ackoff, R.L., 1989. From Data to Wisdom. In Journal of Applied Systems Analysis, vol. 16. pp. 3-9.
  3. Zaim H., 2007. Performance of Knowledge Management Practices: a causal analysis. Knowledge Management, vol. 11(6), pp. 54-67. url:http://dx.doi.org/ 10.1108/13673270710832163.
  4. Miles, I., 2005. Knowledge intensive business services: Prospects and policies. Emerald Group Publishing Limited, vol. 7(6). pp. 39-63.
  5. DOI 10.1108/14636680510630939.
  6. Scarso, E., Bolisani, E., 2010. Knowledge-based strategies for knowledge intensive business services: A multiple case-study of computer service companies . Electronic Journal of Knowledge Management. url: www.ejkm com, vol. 8(1). pp. 151-160.
  7. Tuzovskiy, A. F., 2007. The development of knowledge management systems based on a single ontological knowledge base. In Bulletin of the Tomsk Polytechnic University, vol2(310), pp. 182-185.
  8. Sveiby, Karl-Erik., 1989. The Invisible Balance Sheet. Stockholm, 138 p.
  9. Staab S., Schunurr H-P., Studer R., Sure Y., 2001. Knowledge processes and ontologies. IEEE Intelligent Systems, Special Issue on Knowledge Management, vol. 16(1), pp. 26-34.
  10. NC STI RK. Scientific schools and priorities for the development of the country. URL: http://exclusive.kz/bez-rubriki/22068.
  11. Trubina, I. O., Zabelina, I.N., 2011. Creating personnel's positive motivation in higher education institutions in the process of education and development of scientific schools. In Creative Economy, vol. 1(49), pp. 30-36.
  12. Tuzovskiy, A. F., 2007. Creating and using a knowledge base of specialists' competence profiles at organizations. In Bulletin of the Tomsk Polytechnic University, vol. 310(2), pp. 186-189.
  13. Zhomartkyzy, G., Balova, T., Milosz, M., 2014. Information Models and Methods of the University's Scientific Knowledge Life Cycle Support. Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, pp. 279 - 285.
  14. Adamic, L., Zhang, J., Bakshy, E., Ackerman, M.S., 2008. Knowledge sharing and yahoo answers: everyone knows something. Proceedings of the 17th international conference on World Wide Web. pp. 21- 25.
  15. Baesso, P. T.,Wolfgand, Matsui Siqueira S. and Cristina Vasconcelos de Andrade L., 2014. Finding Reliable People in Online Communities of Questions and Answers.-AnalysisofMetricsandScopeReduction. In Proceedings of the 16th International Conference on Enterprise Information Systems, pp. 526-535, DOI: 10.5220/0004954005260535.
  16. Cantador, I., Castells P., 2011. Extracting multilayered Communities of Interest from semantic user profiles: Application to group modeling and hybrid recommendations . Computers in Human Behavior, vol. 27(4), pp. 1321-1336.
  17. Bolshakova, E. I., Klyshinsky, E. S., Lande, D. V., Noskov, A. A,. Peskova, O. V., Yagunova, E. V., 2011. Automatic processing of natural language texts and computational linguistics: Textbooks. - M.: MIEM, 272 p.
  18. Marmanis, H., Babenko, D., 2011. Algorithms of Intellectual Internet. Best practices for collecting, analyzing and processing data. Trans. from English. - SPb .: Symbol-Plus, 480 p.
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Paper Citation


in Harvard Style

Zhomartkyzy G. and Balova T. (2015). Monitoring the Development of University Scientific Schools in University Knowledge Management . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-097-0, pages 222-230. DOI: 10.5220/0005464202220230


in Bibtex Style

@conference{iceis15,
author={Gulnaz Zhomartkyzy and Tatyana Balova},
title={Monitoring the Development of University Scientific Schools in University Knowledge Management},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2015},
pages={222-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005464202220230},
isbn={978-989-758-097-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Monitoring the Development of University Scientific Schools in University Knowledge Management
SN - 978-989-758-097-0
AU - Zhomartkyzy G.
AU - Balova T.
PY - 2015
SP - 222
EP - 230
DO - 10.5220/0005464202220230