Instantiation to Support the Integration of Logical and Probabilistic Knowledge

Integrating the expressive power of first-order logic with the ability of probabilistic reasoning of Bayesian networks has attracted the interest of many researchers for decades. We present an approach to integration that translates logical knowledge into Bayesian networks and uses Bayesian network composition to build a uniform representation that supports both logical and probabilistic reasoning. In particular, we propose a new way of translation of logical knowledge, relation search, that is easy to understand, simple to implement, and efficient to execute. Grounding is required to generate a Bayesian network in the first-order case. In order to limit the size of the generated Bayesian networks, our prototype restricts the knowledge base to be function-free.