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Ontology Design for Scientific Theories That Make Probabilistic Predictions
January/February 2009 (vol. 24 no. 1)
pp. 27-36
ASCII Text
x 
 
David Poole, Clinton Smyth, Rita Sharma, "Ontology Design for Scientific Theories That Make Probabilistic Predictions," IEEE Intelligent Systems, vol. 24, no. 1, pp. 27-36, January/February, 2009.
 
 
BibTex
x
 
@article{ 10.1109/MIS.2009.15,
author = {David Poole and Clinton Smyth and Rita Sharma},
title = {Ontology Design for Scientific Theories That Make Probabilistic Predictions},
journal ={IEEE Intelligent Systems},
volume = {24},
number = {1},
issn = {1541-1672},
year = {2009},
pages = {27-36},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2009.15},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Ontology Design for Scientific Theories That Make Probabilistic Predictions
IS - 1
SN - 1541-1672
SP27
EP36
EPD - 27-36
A1 - David Poole,
A1 - Clinton Smyth,
A1 - Rita Sharma,
PY - 2009
KW - semantic science
KW - scientific theories
KW - probabilistic predictions
KW - ontologies
KW - Aristotelian definitions
KW - mineral exploration
KW - landslide prediction
VL - 24
JA - IEEE Intelligent Systems
ER -
 
David Poole, University of British Columbia
Clinton Smyth, Georeference Online Ltd.
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.

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Index Terms:
semantic science, scientific theories, probabilistic predictions, ontologies, Aristotelian definitions, mineral exploration, landslide prediction
Citation:
David Poole, Clinton Smyth, Rita Sharma, "Ontology Design for Scientific Theories That Make Probabilistic Predictions," IEEE Intelligent Systems, vol. 24, no. 1, pp. 27-36, Jan.-Feb. 2009, doi:10.1109/MIS.2009.15
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