Temporal knowledge acquisition and modeling

  • Authors:

  • Cyril Faucher;Charles Teissèdre;Jean-Yves Lafaye;Frédéric Bertrand

  • Affiliations:

  • L3i, University of La Rochelle, France;MoDyCo, UMR, Paris Ouest Nanterre La Défense University, CNRS, France and Mondeca, Paris, France;L3i, University of La Rochelle, France;L3i, University of La Rochelle, France

  • Venue:
  • EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
  • Year:
  • 2010

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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.