Advertisement

Semantic Audit Application for Analyzing Business Processes

  • Ildikó SzabóEmail author
  • Katalin Ternai
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 268)

Abstract

Standard regulations are used to assess the compliance of business operations by auditors. This procedure is too time-consuming and Computer Assisted Audit Tools lack of the feature of processing documents semantically in an automatic manner. This paper presents a semantic application which is capable of extracting business process models in the shape of process ontologies from business regulations based on reference process ontologies transformed from process models derived from standard regulations. The application uses ontology matching to discover deviations of a given business operation and creates a transparent report for auditors. This semantic tool has been tested on one of the Internationalization processes in the respect of Erasmus mobility.

Keywords

Audit Business process management Text mining Process ontology Ontology matching 

Notes

Acknowledgements

The authors wish to express their gratitude to Dr. András Gabor, associate professor of the Corvinus University of Budapest, for the great topic and the powerful help provided during the development process.

“This work was conducted using the Protégé resource, which is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health.”

References

  1. 1.
    Braun, R.L., Davis, H.E.: Computer-assisted audit tools and techniques: analysis and perspectives. Manag. Auditing J. 18(9), 725–731 (2003)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow Patterns. Distrib. Parallel Databases 14(1), 5–51 (2003)CrossRefGoogle Scholar
  3. 3.
    Hepp, M., Cardoso, J., Lytras, M.D.: The Semantic Web: Real-World Applications from Industry. Springer, New York (2007). ISBN: 0387485309Google Scholar
  4. 4.
    Koschmider, A., Oberweis, A.: Ontology based business process description. In: Proceedings of the CAiSE, pp. 321–333 (2005)Google Scholar
  5. 5.
    Hepp, M., Roman, D.: An ontology framework for semantic business process management. In: Proceedings of Wirtschaftsinformatik, Karlsruhe, 28 February–2 March 2007 (2007)Google Scholar
  6. 6.
    Maedche, A., et al.: Ontology learning part one — on discovering taxonomic relations from the web. In: Zhong, N., et al. (eds.) Web Intelligence, pp. 301–319. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Alasoud, A., Haarslev, V., Shiri, N.: An effective ontology matching technique. In: An, A., Matwin, S., Raś, Zbigniew, W., Ślęzak, D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 585–590. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-68123-6_63 CrossRefGoogle Scholar
  8. 8.
    Otero-Cerdeira, L., et al.: Ontology matching: a literature review. Expert Syst. Appl. 42(2), 949–971 (2015)CrossRefGoogle Scholar
  9. 9.
    Temesi, J.: Nemzetköziesítés a magyar felsőoktatási intézményekben, Audit materials (2013)Google Scholar
  10. 10.
    Velardi, P., Cucchiarelli, A., Pétit, M.: A taxonomy learning method and its application to characterize a scientific web community. IEEE Trans. Knowl. and Data Eng. 19(2), 80–191 (2007)CrossRefGoogle Scholar
  11. 11.
    Maynard, D., Funk, A., Peters, W.: SPRAT: a tool for automatic semantic pattern-based ontology population. In: Proceedings of the International Conference for Digital Libraries and the Semantic Web (2009)Google Scholar
  12. 12.
    Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: an overview. In: Ontology Learning from Text: Methods, Applications and Evaluation, pp. 3–12. IOS Press (2005)Google Scholar
  13. 13.
    Corcho, O., Fernández-López, M., Gómez-Pérez, A., López-Cima, A.: Building legal ontologies with METHONTOLOGY and WebODE. In: Benjamins, V., Richard, Casanovas, P., Breuker, J., Gangemi, A. (eds.). LNCS (LNAI), vol. 3369, pp. 142–157Springer, Heidelberg (2005). doi: 10.1007/978-3-540-32253-5_9 CrossRefGoogle Scholar
  14. 14.
    Cimiano, P., Völker, J.: Text2Onto–a framework for ontology learning and data-driven change discovery. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005). doi: 10.1007/11428817_21 CrossRefGoogle Scholar
  15. 15.
    Szabó, I.: Future development: towards semantic compliance checking. In: Gábor, A., Kő, A. (eds.) Corporate Knowledge Discovery and Organizational Learning. KMOL, vol. 2, pp. 155–173. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-28917-5_7 CrossRefGoogle Scholar
  16. 16.
    Ternai, K.: Semi-automatic methodology for compliance checking on business processes. In: Kő, A., Francesconi, E. (eds.) EGOVIS 2015. LNCS, vol. 9265, pp. 243–256. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-22389-6_18 CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Department of Information SystemsCorvinus University of BudapestBudapestHungary

Personalised recommendations