Abstract
Several approaches have been proposed for dealing with uncertainty in the Semantic Web (SW). Although probabilistic ontologies (PO) is one of the most promising approach to model uncertainty in ontologies, no support has been offered to ontological engineers on how to create this more complex type of ontologies. This task has proven to be extremely difficult and hard, which motivated the creation of the Uncertainty Modeling Process for Semantic Technologies (UMP-ST), a process that guides users in modeling POs. This paper presents the UMP-ST plug-in, a tool that implements this process and shows how the plug-in, implemented in UnBBayes Framework, overcomes the main problems on modeling probabilistic ontologies: the complexity in creating; the difficulty in maintaining and evolving; and the lack of a centralized tool for documenting these ontologies. The probabilistic ontology for Procurement Fraud Detection and Prevention in Brazil is used to show how the UMP-ST plug-in overcomes these problems. This probabilistic ontology is a proof-of-concept use case created as part of a research project at the Brazilian Office of the Comptroller General (CGU). (A short version of this paper was presented on the URSW 2013 [3]).
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PR-OWL requires a MEBN inference engine to process the additional syntax.
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A plug-in descriptor file is both the main and the minimal content of a UnBBayes plug-in, thus one can create a plug-in composed only by a sole descriptor file.
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An MVC design isolates logic and data from the user interface, by separating the components into three independent categories: Model (data and operations), View (user interface) and Controller (mostly, a mediator, scheduler, or moderator of other classes) [2].
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Design patterns are a set of generic approaches aiming to avoid known problems in software engineering [12].
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Due to space limitation, only part of the whole documentation is going to be presented in this paper. The focus will be on presenting several features available in the UMP-ST plug-in.
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References
Allemang, D., Hendler, J.A.: Semantic Web for the Working Ontologist. Morgan Kaufmann, San Francisco (2008)
Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented Software Architecture. A System of Patterns, vol. 1. Wiley, Chichester (1996)
Carvalho, R.N., Ladeira, M., de Souza, R.M., Matsumoto, S., Da Rocha, H.A., Mendes, G.L.: UMP-ST plug-in: a Tool for documenting, maintaining, and evolving probabilistic ontologies. In: Bobillo, F., Carvalho, R.N., da Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Martin, T., Nickles, M., Pool, M. (eds.) Proceedings of the 9th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2013). CEUR Workshop Proceedings, vol. 1073, pp. 15–26 (2013). CEUR-WS.org
Carvalho, R.N., Laskey, K.B., Costa, P.C.G.: PR-OWL 2.0 – bridging the gap to OWL semantics. In: Bobillo, F., Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Nickles, M., Pool, M. (eds.) URSW 2008-2010/UniDL 2010. LNCS, vol. 7123, pp. 1–18. Springer, Heidelberg (2013)
Carvalho, R.N.: Probabilistic ontology: representation and modeling methodology. Ph.D., George Mason University, Fairfax, VA, USA (2011)
Carvalho, R.N., Santos, L.L., Ladeira, M., Costa, P.C.G.: A GUI tool for plausible reasoning in the semantic web using MEBN. In: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications, ISDA ’07, Los Alamitos, CA, USA, pp. 381–386. IEEE Computer Society, October 2007
Chalupsky, H., MacGregor, R.M., Russ, T.: Powerloom manual (2010)
Costa, P.C.G.: Bayesian semantics for the semantic Web. Ph.D., George Mason University, Fairfax, VA, USA (2005)
Costa, P.C.G., Laskey, K.B., Laskey, K.J.: PR-OWL: a Bayesian framework for the semantic Web. In: Proceedings of the First Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2005), Galway, Ireland, November 2005
da Costa, P.C.G., Laskey, K.B., Laskey, K.J.: PR-OWL: a Bayesian ontology language for the semantic Web. In: da Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Nickles, M., Pool, M. (eds.) URSW 2005 - 2007. LNCS (LNAI), vol. 5327, pp. 88–107. Springer, Heidelberg (2008)
Ding, Z., Peng, Y., Pan, R.: BayesOWL: uncertainty modeling in semantic web ontologies. In: Ma, Z. (ed.) Soft Computing in Ontologies and Semantic Web. Studies in Fuzziness and Soft Computing, vol. 204, pp. 3–29. Springer, Heidelberg (2006). doi:10.1007/978-3-540-33473-6_1
Gamma, E., Helm, R., Johnson, R., Vlissides, J.M.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, New York (1994)
Gennari, J.H., Musen, M.A., Fergerson, R.W., Grosso, W.E., Crubzy, M., Eriksson, H., Noy, N.F., Tu, S.W.: The evolution of protégé: an environment for knowledge-based systems development. Int. J. Hum.-Comput. Stud. 58(1), 89–123 (2003)
Gomez-Perez, A., Corcho, O., Fernandez-Lopez, M.: Ontological Engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web, 1st edn. Springer, Heidelberg (2004)
Gotel, O.C.Z., Finkelstein, C.W.: An analysis of the requirements traceability problem. In: 1994 Proceedings of the First International Conference on Requirements Engineering, pp. 94–101 (1994)
Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence. Chapman & Hall/CRC, Boca Raton (2003)
Ladeira, M., da Silva, D., Vieira, M., Onishi, M., Carvalho, R.N., da Silva, W.: Platform independent and open tool for probabilistic networks. In: Proceedings of the IV Artificial Intelligence National Meeting (ENIA 2003) on the XXIII Congress of the Brazilian Computer Society (SBC 2003), Unicamp, Campinas, Brazil, August 2003
Laskey, K.B.: MEBN: a language for first-order Bayesian knowledge bases. Artif. Intell. 172(2–3), 140–178 (2008)
Mahoney, M.: Network engineering for agile belief network models. IEEE Trans. Knowl. Data Eng. 12(4), 487–498 (2000)
Lukasiewicz, T.: Expressive probabilistic description logics. Artif. Intell. 172(6–7), 852–883 (2008)
Matsumoto, S., Carvalho, R.N., Ladeira, M., da Costa, P.C.G., Santos, L.L., Silva, D., Onishi, M., Machado, E.: UnBBayes: a java framework for probabilistic models in AI. In: Cai, K. (ed.) Java in Academia and Research. iConcept Press, Annerley (2011)
Royce, W.W.: Managing the development of large software systems: concepts and techniques. In: Proceedings of IEEE WESTCON, pp. 1–9 (1970). Reprinted in Proceedings of the Ninth International Conference on Software Engineering, pp. 328–338, March 1987
Scott, K.: The Unified Process Explained. Addison-Wesley Longman Publishing Co Inc., Boston (2002)
Sommerville, I.: Software Engineering, 9th edn. Addison Wesley, Boston (2010)
Wiegers, K.E.: Software Requirements, 2nd edn. Microsoft Press, Redmond (2003)
Yang, Y., Calmet, J.: OntoBayes: an ontology-driven uncertainty model. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), vol. 1, pp. 457–463. IEEE Computer Society (2005)
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The authors gratefully acknowledge full support from the Brazilian Office of the Comptroller General (CGU) for the research reported in this paper.
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Carvalho, R.N., dos Santos, L.L., Ladeira, M., da Rocha, H.A., Mendes, G.L. (2014). UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Framework. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web III. URSW URSW URSW 2012 2011 2013. Lecture Notes in Computer Science(), vol 8816. Springer, Cham. https://doi.org/10.1007/978-3-319-13413-0_1
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