3. Ontology-Based Modelling and Reasoning in Operational Risks

  1. Ron S. Kenett PhD, BSc Chairman CEO Research Professor Professor Associate Director Editor in Chief President4,5 and
  2. Yossi Raanan PhD, BSc Senior Consultant Strategic Partner Senior Lecturer former dean head chairman director6,7
  1. Christian Leibold1,
  2. Hans-Ulrich Krieger PhD Senior Researcher2 and
  3. Marcus Spies PhD Senior Consultant Postdoctoral Fellow Professor Chair Scientific and Technical Director3

Published Online: 19 AUG 2010

DOI: 10.1002/9780470972571.ch3

Operational Risk Management

Operational Risk Management

How to Cite

Leibold, C., Krieger, H.-U. and Spies, M. (2010) Ontology-Based Modelling and Reasoning in Operational Risks, in Operational Risk Management (eds R. S. Kenett and Y. Raanan), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470972571.ch3

Editor Information

  1. 4

    KPA Ltd, Raanana, Israel; University of Turin, Italy

  2. 5

    NYU-Poly, Center for Risk Engineering, New York, USA

  3. 6

    KPA Ltd, Raanana, Israel

  4. 7

    College of Management, Academic Studies, Rishon Lezion, Israel

Author Information

  1. 1

    Munich Ludwig-Maximilians-University, Germany

  2. 2

    Sarland University, Germany

  3. 3

    Berlin Technical University, Germany

Publication History

  1. Published Online: 19 AUG 2010
  2. Published Print: 22 OCT 2010

ISBN Information

Print ISBN: 9780470747483

Online ISBN: 9780470972571

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Keywords:

  • axiomatic ontologies;
  • company ontology;
  • MUSING ontologies;
  • operational risk management;
  • proton ontologies;
  • statistics;
  • temporal ontologies

Summary

Ontologies can be used as a backbone for the integration of expert knowledge and the formalization of project results, including advanced predictive analytics and intelligent access to third-party data, through the integration of semantic technologies. The generic and axiomatic ontologies include the set of adapted Proton ontologies and the temporal ontologies, concepts which are common to all MUSING ontologies. This chapter presents the general application of ontologies as foundation for the representation of knowledge. It uses as a case study, the specific knowledge models developed in the domain of operational risks in MUSING. The conceptual separation of ontologies inherently follows the logical division of the scope and context of the specific knowledge in the project. The resulting MUSING ontology set is a blend of existing, validated ontologies, knowledge formalized in standards and specific requirements from the project applications.

Controlled Vocabulary Terms

statistical data; statistical measures