Section: Software

Knowledge-Based Systems and Semantic Web Systems

The Kasimir System for Decision Knowledge Management

Participants : Nicolas Jay, Jean Lieber [contact person] , Amedeo Napoli, Thomas Meilender.

The objective of the Kasimir system is decision support and knowledge management for the treatment of cancer. A number of modules have been developed within the Kasimir system for editing of treatment protocols, visualization, and maintenance. Kasimir is developed within a semantic portal, based on OWL. KatexOWL (Kasimir Toolkit for Exploiting OWL Ontologies, http://katexowl.loria.fr ) has been developed in a generic way and is applied to Kasimir. In particular, the user interface EdHibou of KatexOWL is used for querying the protocols represented within the Kasimir system.

The software CabamakA (case base mining for adaptation knowledge acquisition) is a module of the Kasimir system. This system performs case base mining for adaptation knowledge acquisition and provides information units to be used for building adaptation rules [123] . Actually, the mining process in CabamakA is implemented thanks to a frequent close itemset extraction module of the Coron platform (see § 5.1.1 ). A semantic wiki for the collaborative edition of decision protocols was developed and is going to be deployed.

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

Participants : Julien Cojan, Valmi Dufour-Lussier, Inaki Fernandez, Emmanuelle Gaillard, Laura Infante-Blanco, Florence Le Ber, Jean Lieber, Amedeo Napoli, Emmanuel Nauer [contact person] , 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 case-based reasoning (CBR), ontology engineering, text mining, text annotation, knowledge representation, and hierarchical classification. 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 http://taaable.fr . After being ranked twice second, in the 2008 and 2009 “Computer Cooking Contests” organized during the ICCBR conference, Taaable won the first price and the adaptation challenge, in 2010. In 2011, no contest was organized but the system has, however, been extended by two new features, both concerning knowledge acquisition using FCA [42] . 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 [45] . The second feature uses FCA for extracting adaptation knowledge, in order to be able to better adapt a recipe to given constraints [47] . Current ongoing work on the Taaable project also includes formal representation of preparations [63] .