Knowledge extraction for improving case retrieval and recipe adaptation - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2011

Knowledge extraction for improving case retrieval and recipe adaptation

Julien Cojan
  • Fonction : Auteur
  • PersonId : 836380
Valmi Dufour-Lussier
  • Fonction : Auteur
  • PersonId : 874752
Emmanuelle Gaillard
  • Fonction : Auteur
  • PersonId : 765221
  • IdRef : 197149065
Jean Lieber
Emmanuel Nauer

Résumé

Taaable 4 is a case-based cooking system which is a contestant in the fourth "Computer Cooking Contest", inheriting most of the features of its previous versions as well as adding new ones. Two new features both concerning knowledge acquisition using formal concept analysis (FCA) are proposed this year. The first feature uses FCA in order to enrich the domain ontology (especially the ingredient hierarchy), making the case retrieval more progressive and more precise. The second feature addresses explicitly the adaptation challenge: given a recipe R and some constraints, how can R be adapted. To compute the best way to adapt R, we propose a FCA-based method for extracting adaptation knowledge. These two knowledge extraction processes exploit additional data (73795 recipes) from the "Recipe Source" database.
Fichier principal
Vignette du fichier
CCC_definitif_2011.pdf (428.75 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00646717 , version 1 (30-11-2011)

Identifiants

  • HAL Id : hal-00646717 , version 1

Citer

Julien Cojan, Valmi Dufour-Lussier, Emmanuelle Gaillard, Jean Lieber, Emmanuel Nauer, et al.. Knowledge extraction for improving case retrieval and recipe adaptation. Computer Cooking Contest Workshop, Sep 2011, London, United Kingdom. ⟨hal-00646717⟩
295 Consultations
198 Téléchargements

Partager

More