  Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism
combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN has
sufficient expressive power for general-purpose knowledge representation and
reasoning. Developing a MEBN model to support a given application is a
challenge, requiring definition of entities, relationships, random variables,
conditional dependence relationships, and probability distributions. When
available, data can be invaluable both to improve performance and to streamline
development. By far the most common format for available data is the relational
database (RDB). Relational databases describe and organize data according to
the Relational Model (RM). Developing a MEBN model from data stored in an RDB
therefore requires mapping between the two formalisms. This paper presents
MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We
identify links between the two languages (RM and MEBN) and define four levels
of mapping from elements of RM to elements of MEBN. These definitions are
implemented in the MEBN-RM algorithm, which converts a relational schema in RM
to a partial MEBN model. Through this research, the software has been released
as a MEBN-RM open-source software tool. The method is illustrated through two
example use cases using MEBN-RM to develop MEBN models: a Critical
Infrastructure Defense System and a Smart Manufacturing System.
