A System for Ontologically-Grounded Probabilistic Matching

This paper is part of a project to match descriptions of real-world instances and probabilistic models, both of which can be described at multiple level of abstraction and detail. We use an ontology to control the vocabulary of the application domain. This paper describes the issues involved in probabilistic matching of hierarchical description of models and instances using Bayesian decision theory, which combines ontologies and probabilities. We have two fielded applications of this framework; one for landslide prediction and one for mineral exploration.