Logic, probability and computation: foundations and issues of statistical relational AI
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Probabilistic relational learning and inductive logic programming at a global scale
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Probabilistic reasoning with undefined properties in ontologically-based belief networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Scientific theories that make predictions about observable quantities can be evaluated by their fit to existing data and can be used for predictions on new cases. The authors' goal is to publish such theories along with observational data and the ontologies needed to enable the interoperation of the theories and the data. This article is about designing ontologies that take into account the defining properties of classes. The authors show how a multidimensional design paradigm based on Aristotelian definitions is natural, can easily be represented in OWL, and can provide random variables that provide structure for theories that make probabilistic predictions. They also show how such ontologies can be the basis for representing observational data and probabilistic theories in their primary application domain of geology.