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Taaable: a system for retrieving and creating new cooking recipes by adaptation

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Taaable: a system for retrieving and creating new cooking recipes by adaptation
Theme : Knowledge-Based Systems and Semantic Web Systems
Participants: Amedeo Napoli
Emmanuel Nauer
Jean Lieber
Julien Cojan
Yannick Toussaint

Taaable is a system whose objectives are to retrieve textual cooking recipes and to adapt these retrieved recipes whenever needed. Suppose that someone is looking for a “leek pie” but has only an “onion pie” recipe: how can the onion pie recipe be adapted?

The Taaable system combines principles, methods, and technologies of knowledge engineering, namely CBR, ontology engineering, text mining, text annotation, knowledge representation, and hierarchical classification [ 35]. Ontologies for representing knowledge about the cooking domain, and a terminological base for binding texts and ontology concepts, have been built from textual web resources. These resources are used by an annotation process for building a formal representation of textual recipes. A CBR engine considers each recipe as a case, and uses domain knowledge for reasoning, especially for adapting an existing recipe w.r.t. constraints provided by the user, holding on ingredients and dish types.

The Taaable system is available on line at In addition, Taaable won the second price in the first "Computer Cooking Contest" [ 87] (European Conference on Case-Based Reasoning, September 2008, Trier, Germany), and in the second “Computer Cooking Contest” (International Conference on Case-Based Reasoning, July 2009, Seattle, USA) [ 88]. In 2010, it won the first price and the adaptation challenge. Indeed, it has proposed two new adaptation approaches: adaptation of quantities and adaptation of recipe text preparations [ 39].


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