Xart: Discovery of correlated arguments of n-ary relations in text

Berrahou Soumia Lilia, Buche Patrice, Dibie Juliette, Roche Mathieu. 2017. Xart: Discovery of correlated arguments of n-ary relations in text. Expert Systems with Applications, 73 : 115-124.

https://doi.org/10.1016/j.eswa.2016.12.028

Article de revue ; Article de recherche ; Article de revue à facteur d'impact

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Quartile : Q1, Sujet : OPERATIONS RESEARCH & MANAGEMENT SCIENCE / Quartile : Q1, Sujet : COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE / Quartile : Q1, Sujet : ENGINEERING, ELECTRICAL & ELECTRONIC

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Economie-gestion

Résumé : Here we present the Xart system based on a three-step hybrid method using data mining approaches and syntactic analysis to automatically discover and extract relevant data modeled as n-ary relations in plain text. A n-ary relation links a studied object with its features considered as several arguments. We addressed the challenge of designing a novel method to handle the identification and extraction of heterogeneous arguments such as symbolic arguments, quantitative arguments composed of numbers and various measurement units. We thus developed the Xart system, which relies on a domain ontology for discovering patterns, in plain text, to identify arguments involved in n-ary relations. The discovered patterns take advantage of different ontological levels that facilitate identification of all arguments and pool them in the sought n-ary relation.

Mots-clés Agrovoc : fouille de textes, fouille de données, analyse de données, banque de données, ontologie, industrie minière, calcium

Mots-clés libres : Text mining, Information extraction, N-ary relation, Ontology, Data mining, Sequential pattern, Quantitative data

Classification Agris : C30 - Documentation et information
U30 - Méthodes de recherche
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : Hors axes (2014-2018)

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