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Probabilistic Relational Models for Operational Risk: A New Application Area and an Implementation Using Domain Ontologies

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Advanced Statistical Methods for the Analysis of Large Data-Sets

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

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Abstract

The application of probabilistic relational models (PRM) to the statistical analysis of operational risk is presented. We explain the basic components of PRM, domain theories and dependency models. We discuss two real application scenarios from the IT services domain. Finally, we provide details on an implementation of the PRM approach using semantic web technologies.

This work has been supported by the European Commission under the umbrella of the MUSING project, contract number 027097, 2006-2010.

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Acknowledgements

The author gratefully acknowledges technical exchanges about practical applications related to IT Operational Risk Management and Credit Risk Management within the MUSING consortium. Special thanks go to Prof. P. Giudici, head of Libero Lenti Laboratory at Pavia University, and Prof. Ron Kenett, Torino University and head of the KPA consultancy (Tel Aviv). The author also acknowledges support by MetaWare S.p.A. of Pisa as overall project coordinator.

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Authors and Affiliations

  1. Department of Computer Science, LMU University of Munich, Munich, Germany

    Marcus Spies (Chair of Knowledge Management)

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  1. Marcus Spies
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Correspondence to Marcus Spies .

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Editors and Affiliations

  1. , Dept. of Statistics, University of Roma "La Sapienza", P.le Aldo Moro 5, Roma, 00185, Italy

    Agostino Di Ciaccio

  2. , Dip. di Metodi Quantitativi e Teoria Eco, University "G. d'Annunzio" of Chieti-Pes, V.le Pindaro 42, Pescara, Italy

    Mauro Coli

  3. Fac. Ciencias, Depto. Estadística e Investigación Opera, Universidad de Granada, Avenida Fuente Nueva s/n, Granada, 18071, Spain

    Jose Miguel Angulo Ibanez

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Spies, M. (2012). Probabilistic Relational Models for Operational Risk: A New Application Area and an Implementation Using Domain Ontologies. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_35

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