First-Order Bayesian Classification with 1BC

In this paper we present 1BC, a first-order Bayesian Classifier. Our approach is to view individuals as structured objects, and to distinguish between structural predicates referring to parts of individuals (e.g. atoms within molecules), and properties applying to the individual or one or several of its parts (e.g. a bond between two atoms). We describe an individual in terms of elementary features consisting of zero or more structural predicates and one property; these features are considered conditionally independent following the usual naive Bayes assumption. 1BC has been implemented in the context of the first-order descriptive learner Tertius, and we describe several experiments demonstrating the viability of our approach.