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Ontology Population via NLP Techniques in Risk Management

Jawad Makki 1 Anne-Marie Alquier 1 Violaine Prince 2
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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 PRIMA1 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 Agency2. 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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Contributor : Violaine Prince-Barbier <>
Submitted on : Friday, March 19, 2010 - 6:00:17 PM
Last modification on : Wednesday, June 9, 2021 - 10:00:12 AM
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  • HAL Id : lirmm-00465555, version 1


Jawad Makki, Anne-Marie Alquier, Violaine Prince. Ontology Population via NLP Techniques in Risk Management. International Journal of Humanities and Social Science (IJHSS), Center for Promoting Ideas (CPI), USA, 2009, 3 (3), pp.212-217. ⟨lirmm-00465555⟩



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