Issue No. 01 - January/February (2009 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIS.2009.15
Clinton Smyth , Georeference Online Ltd.
David Poole , University of British Columbia
Rita Sharma , Georeference Online Ltd.
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.
semantic science, scientific theories, probabilistic predictions, ontologies, Aristotelian definitions, mineral exploration, landslide prediction
Clinton Smyth, David Poole, Rita Sharma, "Ontology Design for Scientific Theories That Make Probabilistic Predictions", IEEE Intelligent Systems, vol. 24, no. , pp. 27-36, January/February 2009, doi:10.1109/MIS.2009.15