Mapping Ontology with Probabilistic Relational Models - An Application to Transformation Processes

Cristina Manfredotti, Cedric Baudrit, Juliette Dibie-Barthélemy, Pierre-Henri Wuillemin

2015

Abstract

Motivated by the necessity of reasoning about transformation experiments and their results, we propose a mapping between an ontology representing transformation processes and probabilistic relational models. These extend Bayesian networks with the notion of class and relation of relational data bases and, for this reason, are well suited to represent concepts and ontologies’ properties. To easy the representation, we exemplify a transformation process as a cooking recipe and present our approach for an ontology in the cooking domain that extends the Suggested Upper level Merged Ontology (SUMO).

References

  1. Bobillo, F., Delgado, M., and Gómez-Romero, J. (2013). Reasoning in fuzzy OWL 2 with delorean. In Uncertainty Reasoning for the Semantic Web II, International Workshops URSW 2008-2010 Held at ISWC and UniDL 2010 Held at FLoC, Revised Selected Papers, pages 119-138.
  2. Buche, P., Dervin, C., Haemmerlé, O., and Thomopoulos, R. (2005). Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules. IEEE T. Fuzzy Systems, 13(3):373-383.
  3. Despres, S. (2014). Construction d'une ontologie modulaire pour l'univers de la cuisine numérique. In Catherine Faron-Zucker. IC - 25émes Journées francophones d'Ingénierie des Connaissances, May 2014, Clermont-Ferrand, France, number 1, pages pp.27- 38.
  4. Devitt, A., Danev, B., and Matusikova, K. (2006). Constructing bayesian networks automatically using ontologies. Applied Ontology, 0.
  5. Doan, A., Halevy, A. Y., and Ives, Z. G. (2012). Principles of Data Integration. Morgan Kaufmann.
  6. Fenz, S. (2012). An ontology-based approach for constructing bayesian networks. Data Knowl. Eng., 73:73-88.
  7. Fridman Noy, N. (2004). Semantic integration: A survey of ontology-based approaches. SIGMOD Record, 33(4):65-70.
  8. Friedman, N., Getoor, L., Koller, D., and Pfeffer, A. (1999). Learning probabilistic relational models. In Dean, T., editor, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 99, Stockholm, Sweden, July 31 - August 6, 1999. 2 Volumes, 1450 pages, pages 1300-1309. Morgan Kaufmann.
  9. Guarino, N., Oberle, D., and Staab, S. (2009). What is an ontology? In Staab, S. and Studer, R., editors, Handbook on Ontologies, International Handbooks on Information Systems, pages 1-17. Springer Berlin Heidelberg.
  10. Helsper, E. M. and van der Gaag, L. C. (2002). Building bayesian networks through ontologies. In van Harmelen, F., editor, Proceedings of the 15th Eureopean Conference on Artificial Intelligence, ECAI'2002, Lyon, France, July 2002, pages 680-684. IOS Press.
  11. Hobbs, J. R. and Pan, F. (2004). An ontology of time for the semantic web. ACM Trans. Asian Lang. Inf. Process., 3(1):66-85.
  12. Ishak, M. B., Leray, P., and Amor, N. B. (2011). A two-way approach for probabilistic graphical models structure learning and ontology enrichment. In Filipe, J. and Dietz, J. L. G., editors, KEOD 2011 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development, Paris, France, 26-29 October, 2011, pages 189-194. SciTePress.
  13. Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press.
  14. Lukasiewicz, T. and Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web, 6(4):291 - 308. Semantic Web Challenge 2006/2007.
  15. Muljarto, A., Salmon, J., Neveu, P., Charnomordic, B., and Buche, P. (2014). Ontology-based model for food transformation processes - application to winemaking. In Closs, S., Studer, R., Garoufallou, E., and Sicilia, M., editors, Metadata and Semantics Research - 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014. Proceedings, volume 478 of Communications in Computer and Information Science, pages 329-343. Springer.
  16. Murphy, K. P. (2002). Dynamic bayesian networks: representation, inference and learning. PhD thesis, University of California, Berkeley.
  17. Pan, R., Ding, Z., Yu, Y., and Peng, Y. (2005). A bayesian network approach to ontology mapping. In Gil, Y., Motta, E., Benjamins, V. R., and Musen, M. A., editors, The Semantic Web - ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10, 2005, Proceedings, volume 3729 of Lecture Notes in Computer Science, pages 563-577. Springer.
  18. Qi, G., Ji, Q., Pan, J. Z., and Du, J. (2010). Possdl - A possibilistic DL reasoner for uncertainty reasoning and inconsistency handling. In The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30 - June 3, 2010, Proceedings, Part II, pages 416-420.
  19. Saïs, F. and Thomopoulos, R. (2014). Ontology-aware prediction from rules: A reconciliation-based approach. Knowl.-Based Syst., 67:117-130.
  20. Torti, L., Wuillemin, P.-H., and Gonzales, C. (2010). Reinforcing the Object-Oriented Aspect of Probabilistic Relational Models. In Proceedings of the 5th Probabilistic Graphical Models, pages 273-280.
  21. Truong, B. A., Lee, Y., and Lee, S. (2005). A unified context model: Bringing probabilistic models to context ontology. In Enokido, T., Yan, L., Xiao, B., Kim, D., Dai, Y., and Yang, L. T., editors, Embedded and Ubiquitous Computing - EUC 2005 Workshops, EUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES, Nagasaki, Japan, December 6-9, 2005, Proceedings, volume 3823 of Lecture Notes in Computer Science, pages 566-575. Springer.
  22. Wuillemin, P. and Torti, L. (2012). Structured probabilistic inference. Int. J. Approx. Reasoning, 53(7):946-968.
  23. Yang, Y. and Calmet, J. (2005). Ontobayes: An ontologydriven uncertainty model. In 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC 2005), 28- 30 November 2005, Vienna, Austria, pages 457-463. IEEE Computer Society.
Download


Paper Citation


in Harvard Style

Manfredotti C., Baudrit C., Dibie-Barthélemy J. and Wuillemin P. (2015). Mapping Ontology with Probabilistic Relational Models - An Application to Transformation Processes . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 171-178. DOI: 10.5220/0005590001710178


in Bibtex Style

@conference{keod15,
author={Cristina Manfredotti and Cedric Baudrit and Juliette Dibie-Barthélemy and Pierre-Henri Wuillemin},
title={Mapping Ontology with Probabilistic Relational Models - An Application to Transformation Processes},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={171-178},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005590001710178},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Mapping Ontology with Probabilistic Relational Models - An Application to Transformation Processes
SN - 978-989-758-158-8
AU - Manfredotti C.
AU - Baudrit C.
AU - Dibie-Barthélemy J.
AU - Wuillemin P.
PY - 2015
SP - 171
EP - 178
DO - 10.5220/0005590001710178