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Amélie Cordier
Researcher at University of Lyon
Publications - 86
Citations - 656
Amélie Cordier is an academic researcher from University of Lyon. The author has contributed to research in topics: Case-based reasoning & Knowledge acquisition. The author has an hindex of 14, co-authored 84 publications receiving 616 citations. Previous affiliations of Amélie Cordier include Claude Bernard University Lyon 1 & Lyon College.
Papers
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TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking
Fadi Badra,Rokia Bendaoud,Rim Bentebibel,Pierre-Antoine Champin,Julien Cojan,Amélie Cordier,Sylvie Desprès,Stéphanie Jean-Daubias,Jean Lieber,Thomas Meilender,Alain Mille,Emmanuel Nauer,Amedeo Napoli,Yannick Toussaint +13 more
TL;DR: This paper presents how the Taaable project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies.
Proceedings Article
Extending Case-Based Reasoning with Traces
TL;DR: This paper proposes to use interaction traces as a knowledge source for CBR systems and shows how it allows us to drive back the current limits of CBR.
Book ChapterDOI
Taaable: A Case-Based System for Personalized Cooking
Amélie Cordier,Valmi Dufour-Lussier,Jean Lieber,Emmanuel Nauer,Fadi Badra,Julien Cojan,Emmanuelle Gaillard,Laura Infante-Blanco,Pascal Molli,Amedeo Napoli,Hala Skaf-Molli +10 more
TL;DR: This chapter describes TAAABLE and its modules, including the CBR engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available, and focuses on the knowledge containers used by the system.
Dissertation
Interactive and Opportunistic Knowledge Acquisition in Case-Based Reasoning
TL;DR: This work proposes a formalisation at general level of interactive knowledge learning in CBR (FIKA), which relies on the reasoning failures which, as they allow to highlight the gaps in the available knowledge, are used to guide the learning process.
Proceedings Article
Trace-Based Reasoning - Modeling Interaction Traces for Reasoning on Experiences.
TL;DR: This paper addresses Trace-Based Reasoning by using Case-based Reasoning (CBR) as a descriptive framework and shows that the exploitation of traces instead of cases as knowledge sources raises very specific challenges.