MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
This work addresses a domain-specific problem for developers needing to build MEBN models from relational data, but it is incremental as it builds on existing formalisms without introducing a new paradigm.
The paper tackles the challenge of developing Multi-Entity Bayesian Network (MEBN) models from relational databases by presenting MEBN-RM, a set of mapping rules and an algorithm that converts a relational schema into a partial MEBN model, with the method illustrated through two example use cases.
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.