Section: New Results

Around the Taaable research project

Participants : Valmi Dufour-Lussier, Emmanuelle Gaillard, Laura Infante Blanco, Florence Le Ber, Jean Lieber, Amedeo Napoli, Emmanuel Nauer.

Keywords:

knowledge representation, description logics, classification-based reasoning, case-based reasoning, belief revision, semantic web

The Taaable project [69] (http://taaable.fr ) has been originally created as a challenger of the Computer Cooking Contest (ICCBR Conference). A candidate to this contest is a system whose goal is to solve cooking problems on the basis of a recipe book (common to all candidates), where each recipe is a shallow XML document with an important plain text part. The size of the recipe book (about 1500 recipes) prevents from a manual indexing of recipes: this indexing is performed using semi-automatic techniques.

Beyond its participation to the CCCs, the Taaable project aims at federating various research themes: case-based reasoning (CBR), information retrieval, knowledge acquisition and extraction, knowledge representation, minimal change theory, ontology engineering, semantic wikis, text-mining, etc. CBR is used to perform adaptation of recipe to user constraints. The reasoning process uses a cooking domain ontology (especially hierarchies of classes) and adaptation rules. The knowledge base used by the inference engine is encoded within a semantic wiki, which contains the recipes, the domain ontology, and adaptation rules.

Minimal change theory and belief revision can be used as tools to support adaptation in CBR, i.e. the source case is modified to be consistent with the target problem using a revision operator. Belief revision was applied to Taaable for the adaptation of recipe preparations [3] , using one of the engines included in the library Revisor (cf. §  5.4.5 ).

As acquiring knowledge from experts is costly, a new approach was proposed to allow a CBR system to use partially reliable, non expert, knowledge from the Web for reasoning [68] [5] . This approach is based on a meta-knowledge model to manage knowledge reliability. This model represents notions such as belief, trust, reputation and quality, as well as their relationships and rules to evaluate knowledge reliability. The reliability estimation is used to filter knowledge with high reliability as well as to rank the results produced by the CBR system, ensuring the quality of results.