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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For the adaptation challenge, the recipe base contains a sole case, the recipe that must be adapted.
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\(\mathtt{cabbage }\) is not a subclass of \(\mathtt{inflorescentvegetable }\) (drumhead cabbages are not flowers), neither is \(\mathtt{inflorescentvegetable }\) a subclass of \(\mathtt{cabbage }\) (artichokes are inflorescent vegetables).
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It can be noticed that at least one repair strategy is always applicable for a selected explanation pattern. Indeed, if the explanation pattern corresponds to a dependence of the form “\(x\) requires \(y\)”, then either \({\mathtt{tgt }} \mathrel {{\models }_\mathtt{DK }}\not y\) or \({\mathtt{tgt }} \not \mathrel {{\models }_\mathtt{DK }}\not y\) holds. If the explanation pattern corresponds to a dependence of the form “\(x\) and \(y\) are incompatible”, then \({\mathtt{tgt }} \mathrel {{\models }_\mathtt{DK }}x\) and \({\mathtt{tgt }} \mathrel {{\models }_\mathtt{DK }}y\) holding simultaneously would mean that \({\mathtt{tgt }}\) contains two incompatible ingredients.
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Amélie Cordier is an assistant professor at the University of Lyon 1. She does her research at the LIRIS Laboratory. She got her Ph.D. from Lyon 1 University. Her main research field is dynamic knowledge engineering. She works with case-based reasoning and trace-based reasoning. She lead the Taaable project it 2008 and 2009 and she organized the Computer Cooking Contest in 2010 and 2011.
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Jean Lieber is an assistant professor of Lorraine Université with a Ph.D. and a habilitation degree in computer science, doing his research at LORIA. His main research field is CBR, with an emphasis on knowledge representation for CBR and adaptation in CBR. He has participated to the Taaable project since the first Computer Cooking Contest (2008) and was the Taaable project leader in 2011.
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Emmanuel Nauer is an assistant professor of Lorraine Université and member of the Orpailleur team, at LORIA. Emmanuel Nauer is currently the leader of the Taaable project, on which he has participated since its beginning. In the Taaable project, he has been in charge of the acquisition of the ontology, of the annotation process of recipes, and of knowledge discovery for improving the results of the CBR system.
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Fadi Badra received a Ph.D. degree in computer science from the University of Nancy in 2009. He is an assistant professor at the University of Paris 13 Bobigny, where he joined the Biomedical Informatics research group (LIM&BIO). Fadi’s research contributions to Taaable concerned techniques to acquire adaptation knowledge, either from the end user, or from the case base by the means of knowledge discovery.
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Julien Cojan is currently an INRIA engineer in the team Wimmics. He works on data extraction from semi-structured textual resources for the semantic web. He has a Ph.D. in computer science from Nancy University in 2011, on the application of belief revision to CBR, that is used in the Taaable system for adapting ingredient quantities.
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Valmi Dufour-Lussier graduated from Montréal University and Nancy 2 University and is currently a Ph.D. candidate in Computer Science at Lorraine University. His area of research is located at the interface between textual CBR and spatio-temporal reasoning. He has been involved in Taaable since 2009, and has led the research on recipe text adaptation.
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Emmanuelle Gaillard graduated from Nancy 2 University, and is currently a Ph.D. student in Computer Science at Lorraine University. Her thesis focus on acquisition and management of meta-knowledge to improve a case-based reasoning system. She works also on adaptation knowledge discovery and applies this work to the Taaable system.
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Laura Infante-Blanco obtained her computing engineer degree in Universidad de Valladolid, Spain, in 2011. She is currently working as an INRIA engineer in the orpailleur team developing a generic ontology guided CBR system. She has been involved in the development of WikiTaaableand she is currently in charge of the wiki management.
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Pascal Molli is full professor at University of Nantes and is head of the GDD Team in LINA research center. He has published more than 80 papers in software engineering, information systems, distributed systems, and computer supported cooperative work (CSCW). He mainly works on collaborative distributed systems and focuses on algorithms for distributed collaborative systems, distributed collaborative systems, privacy and security, and collaborative distributed systems for the Semantic Web.
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Amedeo Napoli is a CNRS senior scientist and has a doctoral degree in Mathematics and an habilitation degree in computer science. He is the scientific leader of the Orpailleur research team at the LORIA laboratory in Nancy. He works in knowledge discovery, knowledge representation, reasoning, and Semantic Web.
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Hala Skaf-Molli received a Ph.D. in computer science from Nancy University in 1997. From 1998 to September 2010, she was an associate professor at Nancy University, LORIA. Since October 2010, she is an associate professor at Nantes University, LINA. She has mainly worked on distributed collaborative systems and social semantic web.
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Cordier, A. et al. (2014). Taaable: A Case-Based System for Personalized Cooking. In: Montani, S., Jain, L. (eds) Successful Case-based Reasoning Applications-2. Studies in Computational Intelligence, vol 494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38736-4_7
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