Bayesian Networks are probabilistic structured representations of domains which have been applied to monitoring and manipulating cause and effects for modelled systems as disparate as the weather, disease and mobile telecommunications networks. Although useful, Bayesian Networks are notoriously difficult to build accurately and efficiently which has somewhat limited their application to real world problems. Ontologies are also a structured representation of knowledge, encoding facts and rules about a given domain. This paper outlines an approach to harness the knowledge and inference capabilities inherent in an ontology model to automate the construction of Bayesian Networks to accurately represent a domain of interest. The approach was implemented in the context of an adaptive, self-configuring network management system in the telecommunications domain. In this system, the ontology model has the dual function of knowledge repository and facilitator of automated workflows and the generated BN serves to monitor effects of management activity, forming part of a feedback look for self-configuration decisions and tasks.
D. Sonntag, P. Wennerberg, P. Buitelaar, and S. Zillner. Cases on Semantic Interoperability for Information Systems Integration: Practices and Applications, chapter 8, IGI Global, Hershey, PA, (2009)
H. Son, B. Kim, T. Kim, D. Lee, and S. Hyun. Distributed, Ambient, and Pervasive Interactions: Proc.\ of the First International Conference (DAPI 2013), Las Vegas, NV, USA, 8028, Springer, Heidelberg, (2013)
S. Staab, T. Walter, G. Gröner, and F. Silva Parreiras. Reasoning Web: Semantic Technologies for Software Engineering, volume 6325 of Lecture Notes in Computer Science, Springer, Berlin, (2010)
F. Riguzzi, E. Bellodi, E. Lamma, and R. Zese. Web Reasoning and Rule Systems: 7th International Conference, RR 2013, Mannheim, Germany, volume 7994 of Lecture Notes in Computer Science, Springer, Heidelberg, (2013)
R. Kontchakov, M. Rodríguez-Muro, and M. Zakharyaschev. Reasoning Web: Semantic Technologies for Intelligent Data Access, volume 8067 of Lecture Notes in Computer Science, Springer, Heidelberg, (2013)
A. Turhan. Reasoning Web: Semantic Technologies for Intelligent Data Access, volume 8067 of Lecture Notes in Computer Science, Springer, Heidelberg, (2013)
A. Polleres, A. Hogan, R. Delbru, and J. Umbrich. Reasoning Web: Semantic Technologies for Intelligent Data Access, volume 8067 of Lecture Notes in Computer Science, Springer, Heidelberg, (2013)
C. Wu, V. Potdar, and E. Chang. Advances in Web Semantics I: Ontologies, Web Services and Applied Semantic Web, volume 4891 of Lecture Notes in Computer Science, Springer, Berlin, (2009)
K. Sycara, and R. Vacul\'ın. Advances in Web Semantics I: Ontologies, Web Services and Applied Semantic Web, volume 4891 of Lecture Notes in Computer Science, Springer, Berlin, (2009)
J. Martinez Lastra, and I. Delamer. Advances in Web Semantics I: Ontologies, Web Services and Applied Semantic Web, volume 4891 of Lecture Notes in Computer Science, Springer, Berlin, (2009)
A. Gal, and P. Shvaiko. Advances in Web Semantics I: Ontologies, Web Services and Applied Semantic Web, volume 4891 of Lecture Notes in Computer Science, Springer, Berlin, (2009)