An NLP-Based Ontology Population for a Risk Management Generic Structure

Jawad Makki (), Anne-Marie Alquier () and Violaine Prince ()
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Jawad Makki: UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse
Anne-Marie Alquier: UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse
Violaine Prince: TEXTE - Exploration et exploitation de données textuelles - LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique

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Abstract: In this paper we propose an NLP-based Ontology Population approach for a Generic Structure instantiation from natural language texts, in the domain of Risk Management. The approach is semi-automatic and based on combined NLP techniques using domain expert intervention for control and validation. It relies on the predicative power of verbs in the instantiation process. It is not domain dependent since it heavily relies on linguistic knowledge. We demonstrate the effectiveness of our method on the ontology of the PRIMA project (supported by the European community) and we populate this generic domain ontology via an available corpus. A first validation of the approach is done through an experiment with Chemical Fact Sheets from Environmental Protection Agency.

Keywords: Ontology Population; Information Extraction; Instance Recognition Rules; POS tagging; Risk Management; Semantic Analysis (search for similar items in EconPapers)
Date: 2008-10-28
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Published in CSTST'08: International Conference on Soft Computing as Transdisciplinary Science and Technology, Oct 2008, Cergy-Pontoise, France, pp.350-356

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:lirmm-00332138

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