Automated construction of an Object-Oriented Bayesian Network (OOBN) class hierarchy

Bayesian networks (BNs) are a widely used probabilistic modelling tool for reasoning under uncertainty, though scaling them up for complex real-world problems can be challenging. Object-Oriented Bayesian Networks (OOBNs) have been proposed to address this challenge, providing modellers with the ability to define hierarchies of classes and use these classes to construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. The object-oriented concept of inheritance supports reuse of existing components, but comes with the challenge of building, and then maintaining, an efficient hierarchy of classes. This paper proposes a supergraph based method that constructs class inheritance hierarchies from a set of OOBN classes; this can be used either to form an initial inheritance hierarchy, or to reform an existing hierarchy into a more efficient one. We also present heuristics to convert a BN to an OOBN class, measures to evaluate a constructed hierarchy and empirical analyses of the proposed approach on synthetic hierarchies and on a real-world OOBN project; results show the algorithm works well in practice.