Hostname: page-component-848d4c4894-8bljj Total loading time: 0 Render date: 2024-06-17T10:29:38.955Z Has data issue: false hasContentIssue false

Ontologies for probabilistic networks: a case study in the oesophageal-cancer domain

Published online by Cambridge University Press:  01 March 2007

EVELINE M. HELSPER
Affiliation:
Department of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands; e-mail: linda@cs.uu.nl
LINDA C. VAN DER GAAG
Affiliation:
Department of Information and Computing Sciences, Utrecht University, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands; e-mail: linda@cs.uu.nl

Abstract

Building a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. As the scope and, hence, the size and complexity of networks are increasing, the need for proper management of the elicited domain knowledge becomes apparent. To study the usefulness of ontologies for this purpose, we constructed an ontology for the domain of oesophageal cancer, based on a real-life probabilistic network for the staging of cancer of the oesophagus and the knowledge elicited for its construction. In this paper, we describe the various components of our ontology and outline the benefits of using ontologies in engineering probabilistic networks.

Type
Original Article
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bangsø, O. & Wuillemin, P.-H. 2000 Top-down construction and repetitive structures representation in Bayesian networks. In Etheredge, J. and Manaris, B. (eds.), Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: AAAI Press, pp. 282–286.Google Scholar
Bylander, T. & Chandrasekaran, B. 1988 Generic tasks for knowledge-based reasoning: the “right” level of abstraction for knowledge acquisition. In Gaines, B. R. and Boose, J. H. (eds.), Knowledge Acquisition for Knowledge-Based Systems, vol. 1. London: Academic Press, pp. 65–77.Google Scholar
ChandrasekaranB., B.,Josephson, J. R., & Benjamins, V. R. 1999 What are ontologies, and why do we need them? IEEE Intelligent Systems and Their Applications 14(1), 2026.Google Scholar
Druzdzel, M. J. & van der Gaag, L. C. 2000 Building probabilistic networks: “Where do the numbers come from?” Guest editors’ introduction. IEEE Transactions on Knowledge and Data Engineering 12(4), 481486.Google Scholar
Farquhar, A., FikesR., R., & Rice, J. 1997 The Ontolingua Server: a tool for collaborative ontology construction. International Journal of Human-Computer Studies 46, 707727.Google Scholar
Fernández-López, M. & Gómez-Pérez, A. 2002 Overview and analysis of methodologies for building ontologies. The Knowledge Engineering Review 17(2), 129156.Google Scholar
Fernández-López, M., Gómez-Pérez, A., Pazos Sierra, J. & Pazos Sierra, A. 1999 Building a chemical ontology using methontology and the ontology design environment. IEEE Intelligent Systems and Their Applications 14(1), 3746.Google Scholar
Genesereth, M. R. & Fikes, R. E. 1992 Knowledge Interchange Format version 3.0. Computer Science Department, Stanford University, Technical Report Logic-92-1.Google Scholar
Gennari, J., Musen, M. A., Fergerson, R. W., Grosso, W. E., Crubezy, M., Eriksson, H., Noy, N. F. & Tu, S. W. 2002 The Evolution of Protégé: An Environment for Knowledge-Based Systems Development. Technical report SMI-2002-0943, Stanford Medical Informatics, Stanford University.Google Scholar
Gruber, Th. R. 1993 A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199220.Google Scholar
Gruber, Th. R. 1995 Towards principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies 43, 907928.Google Scholar
Helsper, E. M. & van der Gaag, L. C. 2002 Building Bayesian networks through ontologies. In van Harmelen, F. (ed.), ECAI 2002: Proceedings of the 15th European Conference on Artificial Intelligence. Amsterdam: IOS Press, pp. 680–684.Google Scholar
Helsper, E. M. & van der Gaag, L. C. 2005 Generic knowledge structures for probabilistic-network engineering. The Third Bayesian Modeling Applications Workshop, during UAI-05, Edinburgh, UK.Google Scholar
Jensen, F. V. 2001 Bayesian Networks and Decision Graphs. New York: Springer-Verlag.Google Scholar
Knublauch, H., Fergerson, R. W., Noy, N. F. & Musen, M. A. 2004 The Protégé OWL Plugin: An open development environment for semantic web applications. In McIlraith, S. A., Plexousakis, D. and van Harmelen, F. (eds.), The Semantic Web — ISWC 2004, Proceedings of the Third International Semantic Web Conference, vol. 3298. Springer-Verlag, Berlin/Heidelberg, pp. 229–243.Google Scholar
Koller, D. & Pfeffer, A. 1997 Object-oriented Bayesian networks. In Geiger, D. and Shenoy, P. (eds.), Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann, pp. 302–313.Google Scholar
Koller, D. & Pfeffer, A. 1998 Probabilistic frame-based systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98). Menlo Park, CA: AAAI Press/The MIT Press, pp. 580–587.Google Scholar
Laskey, K. B. & Mahoney, S. M. 1997 Network fragments: representing knowledge for constructing probabilistic models. In Geiger, D. and Shenoy, P. (eds.), Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann, pp. 334–341.Google Scholar
Laskey, K. B. & Mahoney, S. M. 2000 Network engineering for agile belief network models. IEEE Transactions on Knowledge and Data Engineering 12(4), 487497.Google Scholar
Mahoney, S. M. & Laskey, K. B. 1996 Network engineering for complex belief networks. In Horvitz, E. and Jensen, F. (eds.), Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann, pp. 389–396.Google Scholar
Neil, M., Fenton, N. & Nielsen, L. 2000 Building large-scale Bayesian networks. The Knowledge Engineering Review 15(3), 257284.Google Scholar
Noy, N. F., Fergerson, R. W. & Musen, M. A. 2000 The knowledge model of Protege-2000: combining interoperability and flexibility. In Dieng, R. and Corby, O. (eds.), Knowledge Engineering and Knowledge Management: Methods, Models and Tools, Proceedings of the 12th International Conference, EKAW-2000, vol. 1937. Springer-Verlag, Berlin/Heidelberg, pp. 17–32.Google Scholar
Noy, N. F. & Hafner, C. D. 1997 The state of the art in ontology design: a survey and comparative review. AI Magazine 18(3), 5374.Google Scholar
Rieger, C. & Grinberg, M. 1977 The declarative representation and procedural simulation of causality in physical mechanisms. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence. Los Altos, CA: William Kaufmann, pp. 250–256.Google Scholar
Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W. & Wielinga, B. 2000 Knowledge Engineering and Management: The CommonKADS Methodology. Cambridge, MA: The MIT Press.Google Scholar
Studer, R., Benjamins, V. R. & Fensel, D. 1998 Knowledge engineering: principles and methods. Data and Knowledge Engineering 25, 161197.Google Scholar
Uschold, M. & Gruninger, M. 1996 Ontologies: principles, methods and applications. The Knowledge Engineering Review 11(2), 93136.Google Scholar
Uschold, M., King, M., Moralee, S. & Zorgios, Y. 1998 The enterprise ontology. The Knowledge Engineering Review 13(1), 3189.Google Scholar
van der Gaag, L. C. & Helsper, E. M. 2002 Experiences with modelling issues in building probabilistic networks. In Gómez-Pérez, A. and Benjamins, V. R. (eds.), Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web. Proceedings of EKAW 2002, Lecture Notes in Artificial Intelligence, vol. 2473. Berlin Heidelberg: Springer-Verlag, pp. 21–26.Google Scholar
van der Gaag, L. C., Renooij, S, Witteman, C. L. M., Aleman, B. M. P. and Taal, B. G. 2002 Probabilities for a probabilistic network: a case-study in oesophageal cancer. Artificial Intelligence in Medicine 25(2), 123148.Google Scholar
van Heijst, G., Schreiber, A. Th. and Wielinga, B. J. 1997 Using explicit ontologies in KBS development. International Journal of Human-Computer Studies 46(2/3), 183292.Google Scholar