Taaable: a Case-Based System for personalized Cooking

Amélie Cordier 1, 2 Valmi Dufour-Lussier 3 Jean Lieber 3 Emmanuel Nauer 3 Fadi Badra 4 Julien Cojan 5 Emmanuelle Gaillard 3 Laura Infante-Blanco 3 Pascal Molli 6 Amedeo Napoli 3 Hala Skaf-Molli 7
1 SILEX - Supporting Interaction and Learning by Experience
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 TWEAK - Traces, Web, Education, Adaptation, Knowledge
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
4 LIM&BIO
LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
5 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
6 GDD - Gestion de Données Distribuées [Nantes]
LINA - Laboratoire d'Informatique de Nantes Atlantique
Abstract : TAAABLE is a Case-Based Reasoning (CBR) system that uses a recipe book as a case base to answer cooking queries. TAAABLE participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: CBR, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe TAAABLE and its modules. We first present the CBR engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The TAAABLE system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.
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Amélie Cordier, Valmi Dufour-Lussier, Jean Lieber, Emmanuel Nauer, Fadi Badra, et al.. Taaable: a Case-Based System for personalized Cooking. Montani, Stefania and Jain, Lakhmi C. Successful Case-based Reasoning Applications-2, 494, Springer, pp.121-162, 2014, Studies in Computational Intelligence, 978-3-642-38735-7. ⟨10.1007/978-3-642-38736-4_7⟩. ⟨hal-00912767⟩

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