Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Uncertainty Reasoning for the Semantic Web

International Workshop on Uncertainty Reasoning for the Semantic Web

International Workshop on Uncertainty Reasoning for the Semantic Web

URSW 2006, URSW 2007, URSW 2005: Uncertainty Reasoning for the Semantic Web I pp 143–160Cite as

Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5327))

Abstract

Automated ontology population using information extraction algorithms can produce inconsistent knowledge bases. Confidence values assigned by the extraction algorithms may serve as evidence in helping to repair inconsistencies. The Dempster-Shafer theory of evidence is a formalism, which allows appropriate interpretation of extractors’ confidence values. This chapter presents an algorithm for translating the subontologies containing conflicts into belief propagation networks and repairing conflicts based on the Dempster-Shafer plausibility.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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. Welty, C., Murdock, W.: Towards knowledge acquisition from information extraction. In: 5th International Semantic Web Conference, Athens, GA, USA, November 5-9, 2006, pp. 709–722 (2006)

    Google Scholar 

  2. Popov, B.: KIM - a semantic platform for information extraction and retrieval. Natural language engineering 10, 375–392 (2004)

    Article  Google Scholar 

  3. d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: an inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Iria, J.: Relation extraction for mining the Semantic Web. In: Dagstuhl Seminar on Machine Learning for the Semantic Web, Dagstuhl, Germany (2005)

    Google Scholar 

  5. Horvitz, E.J., Heckerman, D.E., Langlotz, C.P.: A framework for comparing alternative formalisms for plausible reasoning. In: AAAI 1986, pp. 210–214 (1986)

    Google Scholar 

  6. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  7. Haase, P., van Harmelen, F., Huang, Z., Stuckenschmidt, H., Sure, Y.: A framework for handling inconsistency in changing ontologies. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 353–367. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Kalyanpur, A., Parsia, B., Sirin, E., Grau, B.C.: Repairing unsatisfiable concepts in OWL ontologies. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 170–184. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Haase, P., Volker, J.: Ontology learning and reasoning - dealing with uncertainty and inconsistency. In: International Workshop on Uncertainty Reasoning for the Semantic Web (URSW), pp. 45–55 (2005)

    Google Scholar 

  10. Stoilos, G., Stamou, G., Tzouvaras, V., Pan, J.Z., Horrocks, I.: Fuzzy OWL: Uncertainty and the semantic web. In: International Workshop of OWL: Experiences and Directions, ISWC 2005 (2005)

    Google Scholar 

  11. Straccia, U.: Towards a fuzzy description logic for the Semantic Web (preliminary report). In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 167–181. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Ding, Z., Peng, Y.: A probabilistic extension to ontology language OWL. In: 37th Hawaii International Conference On System Sciences, HICSS-37 (2004)

    Google Scholar 

  13. da Costa, P.C.G., Laskey, K.B., Laskey, K.J.: PR-OWL: A Bayesian ontology language for the semantic web. In: Workshop on Uncertainty Reasoning for the Semantic Web, ISWC 2005 (2005)

    Google Scholar 

  14. Li, Y., Bontcheva, K., Cunningham, H.: SVM based learning system for information extraction. Deterministic and Statistical Methods in Machine Learning, 319–339 (2005)

    Google Scholar 

  15. Ciravegna, F., Wilks, Y.: Designing Adaptive Information Extraction for the Semantic Web in Amilcare. In: Annotation for the Semantic Web (2003)

    Google Scholar 

  16. Zhu, J., Uren, V., Motta, E.: ESpotter: Adaptive named entity recognition for web browsing. In: Althoff, K.-D., Dengel, A.R., Bergmann, R., Nick, M., Roth-Berghofer, T.R. (eds.) WM 2005. LNCS, vol. 3782. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. de Campos, L.M., Huete, J.F., Moral, S.: Uncertainty management using probability intervals. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds.) IPMU 1994. LNCS, vol. 945, pp. 190–199. Springer, Heidelberg (1995)

    Chapter  Google Scholar 

  18. Gaifman, H.: A theory of higher order probabilities. In: TARK 1986: Proceedings of the 1986 conference on Theoretical aspects of reasoning about knowledge, pp. 275–292. Morgan Kaufmann Publishers Inc., San Francisco (1986)

    Google Scholar 

  19. Richardson, M., Agrawal, R., Domingos, P.: Trust management for the Semantic Web. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  20. Nickles, M.: Social acquisition of ontologies from communication processes. Journal of Applied Ontology 2(3-4), 373–397 (2007)

    Google Scholar 

  21. Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–95 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  22. Shenoy, P.P.: Valuation-based systems: a framework for managing uncertainty in expert systems. In: Fuzzy logic for the management of uncertainty, pp. 83–104. John Wiley & Sons, Inc., New York (1992)

    Google Scholar 

  23. Shenoy, P.P., Shafer, G.: Axioms for probability and belief-function propagation. In: Readings in uncertain reasoning, pp. 575–610. Morgan Kaufmann Publishers Inc., San Francisco (1990)

    Google Scholar 

  24. Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string metrics for matching names and records. In: KDD Workshop on Data Cleaning and Object Consolidation (2003)

    Google Scholar 

  25. Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  26. Pearl, J.: Bayesian and belief-function formalisms for evidential reasoning: a conceptual analysis. In: Readings in uncertain reasoning, pp. 575–610. Morgan Kaufmann Publishers Inc., San Francisco (1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Milton Keynes, UK

    Andriy Nikolov, Victoria Uren, Enrico Motta & Anne de Roeck

Authors
  1. Andriy Nikolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victoria Uren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enrico Motta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anne de Roeck
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. George Mason University, 4400 University Drive, Fairfax, 22030, Virginia, USA

    Paulo Cesar G. da Costa

  2. Dipartimento di Informatica,, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Claudia d’Amato

  3. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Nicola Fanizzi

  4. George Mason University, VA 22030, Fairfax, USA

    Kathryn B. Laskey

  5. MIRTE Corporation, M/S H305, 7515 Colshire Drive, VA 22102-7508, McLean, USA

    Kenneth J. Laskey

  6. Computing Laboratory, Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD, UK

    Thomas Lukasiewicz

  7. University of Bath, BA2 7AY, Bath, UK

    Matthias Nickles

  8. Convera, Inc., 1921 Gallows Road, Suite 200, VA 22182, Vienna, USA

    Michael Pool

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nikolov, A., Uren, V., Motta, E., de Roeck, A. (2008). Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89765-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89764-4

  • Online ISBN: 978-3-540-89765-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation