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Ontology Population via NLP Techniques in Risk Management

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Author Info

  • Jawad Makki

    ()
    (Université Toulouse 1 - Université des Sciences Sociales - Toulouse I)

  • Anne-Marie Alquier

    ()
    (Université Toulouse 1 - Université des Sciences Sociales - Toulouse I)

  • Violaine Prince

    ()
    (LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - CNRS : UMR5506 - Université Montpellier II - Sciences et Techniques du Languedoc)

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    Abstract

    In this paper we propose an NLP-based method for Ontology Population from texts and apply it to semi automatic instantiate a Generic Knowledge Base (Generic Domain Ontology) in the risk management domain. The approach is semi-automatic and uses a domain expert intervention for validation. The proposed approach relies on a set of Instances Recognition Rules based on syntactic structures, and on the predicative power of verbs in the instantiation process. It is not domain dependent since it heavily relies on linguistic knowledge. A description of an experiment performed on a part of the ontology of the PRIMA project (supported by the European community) is given. A first validation of the method is done by populating this ontology with Chemical Fact Sheets from Environmental Protection Agency . The results of this experiment complete the paper and support the hypothesis that relying on the predicative power of verbs in the instantiation process improves the performance.

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    File URL: http://hal-lirmm.ccsd.cnrs.fr/docs/00/33/21/02/PDF/ICSWE_2008.pdf
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    Bibliographic Info

    Paper provided by HAL in its series Post-Print with number lirmm-00332102.

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    Date of creation: 24 Sep 2008
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    Publication status: Published - Presented, ICSWE: Fifth International Conference on Semantic Web Engineering, 2008, Heidelberg, Germany
    Handle: RePEc:hal:journl:lirmm-00332102

    Note: View the original document on HAL open archive server: http://hal-lirmm.ccsd.cnrs.fr/lirmm-00332102/en/
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    Web page: http://hal.archives-ouvertes.fr/

    Related research

    Keywords: Information Extraction; Instance Recognition Rules; Ontology Population; Risk Management; Semantic Analysis;

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