Formal Ontologies and Uncertainty. In Geographical Knowledge

Matteo Caglioni; Giovanni Fusco

doi:10.6092/1970-9870/2530

Matteo Caglioni Université de Nice Sophia Antipolis/ CNRS, ESPACE UMR7300, France
Giovanni Fusco Université de Nice Sophia Antipolis/ CNRS, ESPACE UMR7300, France

DOI: https://doi.org/10.6092/1970-9870/2530

Keywords: Formal Ontologies, Uncertainty, Geographic Knowledge, Probabilistic Ontologies, Possibilistic Ontologies, Fuzzy Ontologies

Abstract

Formal ontologies have proved to be a very useful tool to manage interoperability among data, systems and knowledge. In this paper we will show how formal ontologies can evolve from a crisp, deterministic framework (ontologies of hard knowledge) to new probabilistic, fuzzy or possibilistic frameworks (ontologies of soft knowledge). This can considerably enlarge the application potential of formal ontologies in geographic analysis and planning, where soft knowledge is intrinsically linked to the complexity of the phenomena under study. The paper briefly presents these new uncertainty-based formal ontologies. It then highlights how ontologies are formal tools to define both concepts and relations among concepts. An example from the domain of urban geography finally shows how the cause-to-effect relation between household preferences and urban sprawl can be encoded within a crisp, a probabilistic and a possibilistic ontology, respectively. The ontology formalism will also determine the kind of reasoning that can be developed from available knowledge. Uncertain ontologies can be seen as the preliminary phase of more complex uncertainty-based models. The advantages of moving to uncertainty-based models is evident: whether it is in the analysis of geographic space or in decision support for planning, reasoning on geographic space is almost always reasoning with uncertain knowledge of geographic phenomena.

Downloads

Download data is not yet available.

Author Biographies

Matteo Caglioni, Université de Nice Sophia Antipolis/ CNRS, ESPACE UMR7300, France

Associate Professor in Urban Geography at the University of Nice Sophia Antipolis, France, at the laboratory UMR7300 ESPACE, he works on the analysis of urban and regional systems, by developing qualitative and quantitative methods and models for the city, its territory and its networks. He took part in two COST Actions about Urban Ontologies (C21) and Semantic Enrichment of 3D City Models (TU0801).

Giovanni Fusco, Université de Nice Sophia Antipolis/ CNRS, ESPACE UMR7300, France

CNRS Senior Research Fellow at the laboratory UMR7300 ESPACE, University of Nice Sophia Antipolis, France, he works on urban morphology, metropolitan development and modelling of uncertain knowledge. He currently directs the CNRS exploratory research project “Geo-Uncertainty. Formalisms and methods for treating uncertain knowledge in geography. Applications to spatial segregation processes in metropolitan areas” (PEPS HuMaIn).

References

Abulaish M., Dey L. (2003) Ontology Based Fuzzy Deductive System to Handle Imprecise Knowledge. In Proceedings of the 4th International Conference on Intelligent Technologies, pp. 271-278.

Bakillah M., Mostafavi M. A. (2011) A Fuzzy Logic Semantic Mapping Approach for Fuzzy Geospatial Ontologies. SEMAPRO 2011 The Fifth International Conference on Advances in Semantic Processing, IARIA, pp. 1-8.

Ban H., Alhqvist O. (2009) Representing and Negotiating Uncertain Geospatial Concepts – Where Are the Exurban Areas? Computers, Environment and Urban Systems, issue 33, pp. 233-246.

Bobillo F., Straccia U. (2010) Fuzzy Ontology Representation using OWL 2. arXiv:1009.3391v3 [cs.LO] 4 Nov 2010, pp. 1-32.

Bouchon-Meunier B. (1994) La logique Floue. Paris, Presses Universitaires de France, 128 p.

Caglioni M., Rabino G. (2012) Ontologies in multi-agent systems for building design the case of risk management inside a stadium. In Billen R., Caglioni M., Marina O., Rabino G., San José R. (Eds.) 3D Issues in Urban and Environmental Systems. Società Editrice Esculapio, Bologna, pp. 111-118.

Caglioni M., Rabino G. (2007) Theoretical approach to urban ontology: a contribution from urban system analysis. Published in Teller J., Lee J. R., Roussey C. (Eds.) Ontologies for Urban Development of Studies in Computational Intelligence, Volume 61, Springer Berlin / Heidelberg, pp. 109-119.

Costa P.C.G., Laskey K.B., Laskey K.J. (2008) PR-OWL: A Bayesian Ontology Language for the Semantic Web. LNAI 5327, Springer-Verlag Berlin Heidelberg, pp. 88-107.

De Runz C. (2008) Imperfection, temps et espace: modélisation, analyse et visualisation dans un SIG archéologique. Thèse de doctorat de l’Université de Reims Champagne-Ardenne (URCA), 230 p.

Dempster A.P. (1968) A generalization of Bayesian inference. Royal Statistical Society Journal, 30(2) pp. 205–247.

Dempster A.P., Laird N.M., Rubin D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J R Stat Soc Series B (Statistical Methodology), n. 39, pp. 1–38.

Ding Z., Peng Y. (2004) A Probabilistic Extension to Ontology Language OWL. Proceedings of the 37th Hawaii International Conference on System Sciences, pp. 1-10.

Dubois D., Prade H. (1985) Théorie des Possibilités, Paris, Masson

Dubois D., Lang J., Prade H. (1994) Possibilistic logic. In Handbook of logic in Artificial Intelligence and Logic Programming, Volume 3, Oxford University Press, pp. 439–513.

Fisher P., Comber A., Wadsworth R. (2006) Approaches to Uncertainty in Spatial Data. In R. Devillers and R. Jeansoulin (Eds.) Fundamentals of Spatial Data Quality, pp. 43–59.

Fonseca F., Camara G., Monteiro A.M. (2005) A Framework for Measuring the Interoperability of Geo-Ontologies. In Spatial Cognition and Computation, vol. 6, issue 4, 2005, pp. 307-329.

Fusco G. (2004) Looking for Sustainable Urban Mobility through Bayesian Networks. Cybergeo, http://cybergeo.revues.org/2777

Fusco G. (2012) Démarche géo-prospective et modélisation causale probabiliste. Cybergéo, http://cybergeo.revues.org/25423

Ghorbel H., Bahri A., Bouaziz R. (2008) A Framework for Fuzzy Ontology Models. Proceedings of Journées Francophones sur les Ontologies JFO’2008, Lyon, France, pp. 21-30.

Goodchild M.F. (2007) Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0. International Journal of Spatial Data Infrastructures Research, 2, pp. 24–32.

Loiseau Y., Boughanem M., Prade H. (2006) Evaluation of Term-based Queries using Possibilistic Ontologies. Soft Computing in Web Information Retrieval, Studies in Fuzziness and Soft Computing, Volume 197, pp 135-160.

Murgante B., Scorza F. (2011) Ontology and Spatial Planning. ICCSA 2011, Part II, LNCS 6783, pp. 255–264.

Qi G., Ji Q., Pan J.Z., Du J. (2010) PossDL - A Possibilistic DL Reasoner for Uncertainty Reasoning and Inconsistency Handling. In L. Aroyo et al. (Eds.), ESWC 2010, LNCS 6089, pp. 416–420.

Shafer G. (1976) A Mathematical Theory of Evidence. Princeton University Press, Princeton, N.J., USA.

Straccia U. (2006) A Fuzzy Description Logic for the semantic Web. Proceedings in Capturing Intelligence: Fuzzy logic and the semantic Web, Elie Sanchez ed., Elsevier.

Studer R., Benjamins V., Fensel D. (1998). Knowledge Engineering: Principles and Methods. Data and Knowledge Engineering, 25(1-2), pp. 161-197.

Walker W. et al. (2003), Defining Uncertainty. A Conceptual Basis for Uncertainty Management in Model-Based Decision Support. Integrated Assessment, vol. 4, no 1, pp. 5-17

Wang Y., Liu W., Bell D. (2007) Combining Uncertain Outputs from Multiple Ontology Matches, in H. Prade and V.S. Subrahmian (Eds.), SUM 2007, LNAI 4772, Springer, pp. 201-214

Zadeh L.A. (1965) Fuzzy sets. Information and Control, vol. 8, no 3, pp. 338-353.

Zadeh L.A. (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, pp. 3–28.

Zhu M., Gao Z., Pan J.Z., Zhao Y., Xu Y., Quan Z. (2013) Ontology Learning from Incomplete Semantic Web Data by BelNet. In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2013), pp. 761-768.