Section: New Results

Around the Taaable research project

Participants : Valmi Dufour-Lussier, Emmanuelle Gaillard, 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 was originally created as a challenger of the Computer Cooking Contest (ICCBR Conference) [4] (http://taaable.fr ). A candidate to this contest is a system whose goal is to solve cooking problems.

Beyond its participation to the CCC challenges, 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 performs adaptation of recipes w.r.t. user constraints. The reasoning process is based on a cooking domain ontology (especially hierarchies of classes) and adaptation rules. The knowledge base is encoded within a semantic wiki containing 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 to compute ingredient substitutions and to adjust the ingredient quantities [65] using engines included in the Revisor library (see §  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. 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. Performing CBR with knowledge resulting from an e-community is improved by taking into account the knowledge reliability [64] .

Taaable won in 2014 the CCC originality challenge for all the open resources that the Taaable team developed during the last years for the CBR community: WikiTaaable, a semantic wiki containing cooking domain knowledge, Tuuurbine, a generic ontology guided CBR engine over RDFS (see §  5.4.3 ), and Revisor, an adaptation engine implementing various revision operators (see §  5.4.5 ).