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Issue No.01 - January/February (2009 vol.24)
pp: 27-36
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
David Poole, Rita Sharma, "Ontology Design for Scientific Theories That Make Probabilistic Predictions", IEEE Intelligent Systems, vol.24, no. 1, pp. 27-36, January/February 2009, doi:10.1109/MIS.2009.15
1. J. Hendler, "Science and the Semantic Web," Science, vol. 299, no. 5606, 2003, pp. 520–521; 5606520?ijkey=1BUgJQXW4nU7Q&keytype=ref&siteid=sci .
2. P. Fox et al., "Semantically Enabled Large-Scale Science Data Repositories," Proc. 5th Int'l Semantic Web Conf. (ISWC 06), LNCS 4273, Springer, 2006, pp. 792–805; .
3. D. Poole, C. Smyth, and R. Sharma, "Semantic Science: Ontologies, Data and Probabilistic Theories," Uncertainty Reasoning for the Semantic Web I, P.C. da Costa et al., eds., LNAI/LNCS 5327, Springer, 2008; .
4. L. Getoor, and B. Taskar eds., Introduction to Statistical Relational Learning, MIT Press, 2007.
5. L. De Raedt et al., eds., Probabilistic Inductive Logic Programming, Springer, 2008.
6. E.T. Jaynes, Probability Theory: The Logic of Science, Cambridge Univ. Press, 2003; .
7. C. Howson, and P. Urbach, Scientific Reasoning: The Bayesian Approach, 3rd ed., Open Court, 2006.
8. P.F. Patel-Schneider, P. Hayes, and I. Hor-rocks, OWL Web Ontology Language: Semantics and Abstract Syntax, World Wide Web Consortium (W3C) Recommendation, Feb. 2004;
9. B. Smith, "Ontology," Blackwell Guide to the Philosophy of Computing and Information, L. Floridi ed., Blackwell, 2003, pp. 155–166; ontology_pic.pdf.
10. T. Lukasiewicz, "Expressive Probabilistic Description Logics," Artificial Intelligence, vol. 172, nos. 6–7, 2008, pp. 852–883.
11. P.C.G. da Costa, K.B. Laskey, and K.J. Laskey, "PR-OWL: A Bayesian Ontology Language for the Semantic Web," Proc. ISWC Workshop Uncertainty Reasoning for the Semantic Web, 2005; Publications/CEUR-WS/Vol-173.
12. K. Popper, The Logic of Scientific Discovery, Basic Books, 1959.
13. C. Dilworth, "On Theoretical Terms," Erkenntnis [Cognition], vol. 21, no. 3, 1984, pp. 405–421.
14. B. Smith, "The Logic of Biological Classification and the Foundations of Biomedical Ontology," Invited Papers from the 10th Int'l Conf. Logic Methodology and Philosophy of Science, Elsevier North-Holland, 2003, pp. 190–201; .
15. Aristotle, Categories, E.M. Edghill trans.; .
16. M.R. Gillespie and M.T. Styles, BGS Rock Classification Scheme, Vol. 1: Classification of Igneous Rocks, research report RR 99-06, British Geological Survey, 1999;
17. L. Struik et al., A Preliminary Scheme for Multihierarchical Rock Classifica-tion for Use with Thematic Computer-Based Query Systems, Current Research 2002-D10, Geological Survey of Canada, 2002; handoutsGSC_D10_2002.pdf.
18. S. Richard et al., "Lithology Categories Vocabulary," SEE GRID community, 2008; LithologyCategories.
19. C. Boutilier et al., "Context-Specific Independence in Bayesian Networks," Proc. 12th Ann. Conf. Uncertainty in Artificial Intelligence (UAI 96), Morgan Kaufmann, 1996, pp. 115–123.
20. J. Łukasiewicz, "On Three-Valued Logic" (in Polish), Ruch Filozoficzny [Philosophical Movement], vol. 5, 1920, pp. 170–171. English translation in Jan Łukasiewicz Selected Works, L. Borkowski, ed., North-Holland and Polish Scientific Publishers, 1970.
21. R. Sharma, D. Poole, and C. Smyth, A Framework for Ontologically Grounded Probabilistic Matching, tech. report, Dept. of Computer Science, Univ. of British Columbia, 2008; .
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