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Extraction et reconstitution de relation n-Aires issues d'articles scientifiques en domaine expérimental guideées par une Ressource Termino-Ontologique

Lentschat M., Buche P., Dibie J., Roche M.. 2022. In : Amer-Yahia Sihem (ed.), Soulet Arnaud (ed.). Actes de la conférence EGC'2022. s.l. : Éditions RNTI, p. 497-498. (Revue des Nouvelles Technologies de l'Information, RNTI-E-38). Conférence extraction et gestion des connaissances (EGC 2022). 22, 2022-01-24/2022-01-28, Blois (France).

DOI: 10.18167/DVN1/GCZBC9

We present different approaches to construct n-Ary relation instances from scientific articles in experimental domains (i.e. food packaging) driven by a Termino-Ontological Resource dealing with the food packaging domain. Our method starts with the identification of partial n-Ary relations in the document tables and seeks to complete them with argument instances from text of the articles. Our approach is based on a new multi-feature representation (i.e. STaRe - Scientific Table Representation) and the n-Ary relation reconstruction based on text-mining methods.

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