Temporal Knowledge Acquisition and Modeling

  • Cyril Faucher
  • Charles Teissèdre
  • Jean-Yves Lafaye
  • Frédéric Bertrand
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6317)

Abstract

The objectives of this paper are to present, describe, and explain the foundations and the functionalities of a temporal knowledge acquisition and modeling solution workflow, which aims at acquiring temporal knowledge from texts in order to populate a constrained object model. We are using several models for temporal data, one of which is generic and employed as a pivot model between a linguistic representation and a calendar representation. The approach we propose is generic and has been tested against a real use case, in which input data is made of temporal properties defining when a given location (a theater, a restaurant, a shopping center, etc.) is open or closed. Most expressions entered are expressed in intension. Our models provide a core support to the system that linguistically analyses data entries, transforms them into extensive calendar information and allow users to control the quality of the system’s interpretation.

Keywords

Temporal Knowledge Acquisition Temporal Data Modeling Linguistic Annotation Model Driven Engineering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    International Organization for Standardization: Text of 19108 Geographic information - Temporal schema, 55 p. (2002)Google Scholar
  2. 2.
    Hobbs, J.R., Pan, F.: An Ontology of Time for the Semantic Web. ACM TALIP. Special Issue on Temporal Information Processing 3(1), 66–85 (2004)Google Scholar
  3. 3.
    Dawson, F., Stenerson, D.: Internet Calendaring and Scheduling Core Object Specification (iCalendar) - RFC2445, RFC Editor (1998)Google Scholar
  4. 4.
    Carnap, R.: Meaning and Necessity. University of Chicago Press, Chicago (1947)MATHGoogle Scholar
  5. 5.
    Schmidt, D.C.: Model-Driven Engineering. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  6. 6.
    Bontcheva, K., Cunningham, H.: The Semantic Web: A New Opportunity and Challenge for Human Language Technology. In: 2nd ISWC Proceedings, Workshop on HLT for The Semantic Web and Web Services, Florida, October 20-23, pp. 89–96 (2003)Google Scholar
  7. 7.
    Pustejovsky, J., Castano, J., Ingria, R., Sauri, R., Gaizauskas, R., Setzer, A., Katz, G.: TimeML: Robust Specification of Event and Temporal Expressions in Text. In: IWCS Proceedings, Florida (2003)Google Scholar
  8. 8.
    Mani, I., Wilson, G.: Robust temporal processing of news. In: Proceedings of the 38th ACL, Hong Kong, pp. 69–76 (2000)Google Scholar
  9. 9.
    Setzer, A., Gaizauskas, R.: Annotating Events and Temporal Information in Newswire Texts. In: 2nd LREC Proceedings, Athens, pp. 64–66 (2000)Google Scholar
  10. 10.
    Schilder, F., Habel, C.: From Temporal Expressions to Temporal Information: Semantic Tagging of News Messages. In: ACL 2001 Proceedings, Workshop on Temporal and Spatial Information Processing, Toulouse, pp. 65–72 (2001)Google Scholar
  11. 11.
    Benveniste, E.: Problèmes de linguistique générale. In: Gallimard (ed.), Paris, vol. 2 (1974)Google Scholar
  12. 12.
    Bézivin, J.: On The Unification Power of Models. Software and System Modeling 4(2), 171–188 (2005)CrossRefGoogle Scholar
  13. 13.
    Battistelli, D., Couto, J., Minel, J.-L., Schwer, S.: Representing and Visualizing calendar expressions in texts. In: STEP 2008, Venise (2008)Google Scholar
  14. 14.
    Teissèdre, C., Battistelli, D., Minel, J.-L.: Resources for Calendar Expressions Semantic Tagging and Temporal Navigation through Texts. In: 7th LREC, Valletta, May 19-21 (2010)Google Scholar
  15. 15.
    Reiter, E., Dale, R.: Building Natural Language Generation Systems. Journal of Natural Language Engineering, 3 Part 1 (1999)Google Scholar
  16. 16.
    OMG, Unified Modeling Language (UML): Superstructure. Version 2.2 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cyril Faucher
    • 1
  • Charles Teissèdre
    • 2
    • 3
  • Jean-Yves Lafaye
    • 1
  • Frédéric Bertrand
    • 1
  1. 1.L3iUniversity of La RochelleFrance
  2. 2.MoDyCo – UMR 7114Paris Ouest Nanterre La Défense University – CNRSFrance
  3. 3.MondecaParisFrance

Personalised recommendations