BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATAConsidering Administrative Management Processes

Anna Rozeva, Martin Ivanov, Roumiana Tsankova

Abstract

The aim of the paper is to present a framework for designing business model for knowledge generation from explicit data on “good” administrative management practices. Knowledge discovery demands the availability and access to high volumes of data. There is such data collected in databases and files in different formats. Knowledge extraction is performed by statistical and machine learning mining methods of text mining and data mining. The proposed framework for business model design consists of structure and knowledge models. The two models refer to the text transformation and the knowledge extraction phases respectively. Structure model implements text mining methods for converting the text documents into structured objects. These objects form a data mining structure that is the source for the knowledge discovery models. They are oriented to descriptive and predictive modelling tasks which concern document clustering and categorization. The business model framework is trained on source text documents for “good” practices in public administration and business management which are classified according to preliminary established topics. The results obtained by the descriptive and predictive knowledge models are presented.

References

  1. Amardeilh, F., Laublet, P., 2005. Document annotation and ontology population from linguistic extractions, In Proceedings of the 3rd international conference on knowledge capture, ACM New York, NY, USA.
  2. Castellano, M., Mastronardi, G., 2007. A web text mining flexible architecture, In: Proceedings of World Academy of Science, Engineering and Technology, IV Int. Conf. on Computer, Electrical, and Systems Science, and Engineering Cesse Edited by:PWASET Vol. 26, 78-85.
  3. Cios, K., Pedrycz, W., 2000. Data mining methods for knowledge discovery, Kluwer Academic Publishers, Netherlands, 3rd edition.
  4. Faulstich, L., Stadler, P., et al, 2003. litsift: Automated text categorization in bibliographic search. In Data mining and text mining for bioinformatics Workshop at the ECML/PKDD Dubrovnik-Cavtat, Croatia, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10. 1.1.10.8737&rep=rep1&type=pdf , Retrieved May, 16th 2011.
  5. Hall, M., Frank, E., 2009. The WEKA data mining software: an update, ACM SIGKDD Explorations newsletter, Vol.11, Issue 1, New York, NY, USA
  6. Hand, D., Mannila, H., 2001. Principles of data mining, MIT Press.
  7. Hotho, A., Nurnberger, A., 2005. A brief survey of text mining, LVD Forum, Band 20, pp.19-62.
  8. Larose, D., 2006. Data mining methods and models, Wiley-IEEE Press.
  9. Maedche, A., Staab, S., 2000. Mining ontologies from text, In R. Dieng and O. Corby (eds.): EKAW 2000, LNAI 1937, Springer Verlag Berlin Heidelberg.
  10. Nisbet, R., Elder, J., 2009. Handbook of Statistical Analysis and Data Mining Applications, AP Elsevier Inc.
  11. Spasic, A., Ananiadou, A., 2005. Text mining and ontologies in biomedicine: Making sense of raw text, Briefings in bioinformatics, Vol.6, No3,Henry Steward Publications.
  12. Stavrianou, A., Andritsos, P., Nicoloyannis, N., 2007. Overview and semantic issues of text mining, SIGMOD Record September 2007 (Vol.36, No3)
  13. Tsankova, R., Rozeva, A., 2011. Generation of knowledge from “good practices” as open government procedure, In CeDEM11 Proceedings of the International conference for e-democracy and open government, Peter Parychek, Manuel Kripp (eds), Danube University Krems, Austria, 209-219
  14. Uramoto, N., Matsuzava, H., 2004. A text mining system for knowledge discovery from biomedical documents, IBM Systems Journal, vol.43, No3.
  15. Witten, I., Frank, E., 2011. Data Mining: Practical machine learning tools and technique, Morgan Kaufmann Publishers. Burlington, 3rd edition.
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Paper Citation


in Harvard Style

Rozeva A., Ivanov M. and Tsankova R. (2011). BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATAConsidering Administrative Management Processes . In Proceedings of the First International Symposium on Business Modeling and Software Design - Volume 1: BMSD, ISBN 978-989-8425-68-3, pages 114-121. DOI: 10.5220/0004459201140121


in Bibtex Style

@conference{bmsd11,
author={Anna Rozeva and Martin Ivanov and Roumiana Tsankova},
title={BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATAConsidering Administrative Management Processes},
booktitle={Proceedings of the First International Symposium on Business Modeling and Software Design - Volume 1: BMSD,},
year={2011},
pages={114-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004459201140121},
isbn={978-989-8425-68-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Symposium on Business Modeling and Software Design - Volume 1: BMSD,
TI - BUSINESS MODELLING FOR GENERATION OF KNOWLEDGE FROM EXPLICIT DATAConsidering Administrative Management Processes
SN - 978-989-8425-68-3
AU - Rozeva A.
AU - Ivanov M.
AU - Tsankova R.
PY - 2011
SP - 114
EP - 121
DO - 10.5220/0004459201140121