Skip to main content

OntoBayes Approach to Corporate Knowledge

  • Conference paper
  • 1114 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4203))

Abstract

In this paper, we investigate the integration of virtual knowledge communities (VKC) into an ontology-driven uncertainty model (OntoBayes). The selected overall framework for OntoBayes is the multiagent paradigm. Agents modeled with OntoBayes have two parts: knowledge and decision making parts. The former is the ontology knowledge while the latter is based upon Bayesian Networks (BN). OntoBayes is thus designed in agreement with the Agent Oriented Abstraction (AOA) paradigm. Agents modeled with OntoBayes possess a common community layer that enables to define, describe and implement corporate knowledge. This layer consists of virtual knowledge communities.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Yang, Y., Calmet, J.: Ontobayes: An ontology-driven uncertainty model. In: Proceeding of the International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC 2005), vol. I, pp. 457–464. IEEE Computer Society Press, Vienna, Austria (2005)

    Google Scholar 

  2. Pearl, J.: Probabilistic reasoning in intelligent systems, 2nd edn. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  3. Russell, S.J., Norvig, P.: Artificial intelligence: A Modern Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2003)

    Google Scholar 

  4. Shachter, R., Peot, M.: Simulation approaches to general probabilistic inference on belief networks. In: Proceedings of the 5th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1990), pp. 221–234. Elsevier Science Publishing Company, Inc., New York (1990)

    Google Scholar 

  5. Bouckaert, R.R., Castillo, E., Gutiérrez, J.M.: A modified simulation scheme for inference in bayesian networks. International Journal of Approximate Reasoning 14, 55–80 (1996)

    Article  MATH  Google Scholar 

  6. Yang, Y., Calmet, J.: Decision making in ontology-based uncertainty model. In: 21st European Conference on Operational Research (2006)

    Google Scholar 

  7. Calmet, J., Maret, P., Endsuleit, R.: Agent-oriented abstraction. Revista (Real Academia de Ciencias, Serie A de Matematicas), Special Issue on Symbolic Computation and Artificial Intelligence 98, 77–84 (2004)

    MATH  Google Scholar 

  8. Weber, M.: Economy and society. University of California Press (1986)

    Google Scholar 

  9. Maret, P., Calmet, J.: Modeling corporate knowledge within the agent oriented abstraction. In: Proc. International Conference on Cyberworlds (CW 2004), pp. 224–231. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  10. Maret, P., Hammond, M., Calmet, J.: Virtual knowledge communities for corporate knowledge issues. In: Gleizes, M.-P., Omicini, A., Zambonelli, F. (eds.) ESAW 2004. LNCS (LNAI), vol. 3451, pp. 33–44. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Hammond, M.: Virtual knowledge communities for distributed knowledge management: A multi-agent-based approach using jade. Master’s thesis, University of Karlsruhe and Imperial College London (2004)

    Google Scholar 

  12. Maret, P., Calmet, J.: Corporate knowledge in cyberworlds. IEICE Transaction, Information and Systems Society E88-D, 880–887 (2005)

    Article  Google Scholar 

  13. Bonifacio, M., Bouquet, P., Cuel, R.: Knowledge nodes: the building blocks of a distributed approach to knowledge management. Journal of Universal Computer Science 8, 652–661 (2002)

    Google Scholar 

  14. Fischer, G., Ostwald, J.: Knowledge management: Problems, promises, realities, and challenges. IEEE Intelligent Systems 16, 60–72 (2001)

    Article  Google Scholar 

  15. W3C (OWL Web Ontology Language Guide), http://www.w3.org/TR/owl-guide

  16. W3C (Resource Description Framework (RDF): Concepts and Abstract Syntax), http://www.w3.org/TR/rdf-concepts

  17. Jensen, F.V.: An introduction to Bayesian networks. UCL Press (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, Y., Calmet, J. (2006). OntoBayes Approach to Corporate Knowledge. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_31

Download citation

  • DOI: https://doi.org/10.1007/11875604_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45764-0

  • Online ISBN: 978-3-540-45766-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics