Welcome to the OpenMath website. OpenMath is an extensible standard for representing the semantics of mathematical objects. If you haven't heard about it before you might want to consult the overview.
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.
Proceedings of KDD Cup and Workshop 2007
The Workshop was Co-organized by ACM SIGKDD and Netflix
Held during KDD-2007, San Jose, California, Aug 12, 2007
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K. Cheung, H. Frost, M. Marshall, E. Prud'hommeaux, M. Samwald, J. Zhao, und A. Paschke. BMC Bioinformatics, (2009)"We have explored a tool called "FeDeRate", which enables a global SPARQL query to be decomposed into subqueries against the remote databases offering either SPARQL or SQL query interfaces.".