Emmanuelle Gaillard

Taaable: a Case-Based System for personalized Cooking

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.

Tuuurbine: A Generic CBR Engine over RDFS

This paper presents Tuuurbine, a case-based reasoning (CBR) system for the Semantic Web. Tuuurbine is built as a generic CBR system able to reason on knowledge stored in RDF format; it uses Semantic Web technologies like RDF/RDFS, RDF stores, SPARQL, and optionally Semantic Wikis. Tuuurbine implements a generic case-based inference mechanism in which adaptation consists in retrieving similar cases and in replacing some features of these cases in order to obtain one or more solutions for a given query. The search for similar cases is based on a generalization/specialization method performed by means of generalization costs and adaptation rules. The whole knowledge (cases, domain knowledge, costs, adaptation rules) is stored in an RDF store.

How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability

This paper shows that performing case-based reasoning (CBR) on knowledge coming from an e-community is improved by taking into account knowledge reliability. MKM (meta-knowledge model) is a model for managing reliability of the knowledge units that are used in the reasoning process. For this, MKM uses meta-knowledge such as belief, trust and reputation, about knowledge units and users. MKM is used both to select relevant knowledge to conduct the reasoning process, and to rank results provided by the CBR engine according to the knowledge reliability. An experiment in which users perform a blind evaluation of results provided by two systems (with and without taking into account reliability, i.e. with and without MKM) shows that users are more satisfied with results provided by the system implementing MKM.

Improving Case Retrieval Using Typicality

This paper shows how typicality can be used to improve the case retrieval of a case-based reasoning (CBR) system, improving at the same time the global results of the CBR system. Typicality discriminates subclasses of a class in the domain ontology depending of how a subclass is a good example for its class. Our approach proposes to partition the subclasses of some classes into atypical, normal and typical subclasses in order to refine the domain ontology. The refined ontology allows a finer-grained generalization of the query during the retrieval process. The benefits of this approach are presented according to an evaluation in the context of Taaable, a CBR system designed for the cooking domain.

Case-Based Reasoning on E-Community Knowledge

This paper presents MKM, a meta-knowledge model to manage knowledge reliability, in order to extend a CBR system so that it can reason on partially reliable, non expert, knowledge from the Web. Knowledge reliability is considered from the point of view of the decision maker using the CBR system. It is captured by the MKM model including notions such as belief, trust, reputation and quality, as well as their relationships and rules to evaluate knowledge reliability. We detail both the model and the associated approach to extend CBR. Given a problem to solve for a specific user, 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.

Man-machine collaboration to acquire cooking adaptation knowledge for the TAAABLE case-based reasoning system

This paper shows how humans and machines can better collaborate to acquire adaptation knowledge (AK) in the framework of a case-based reasoning (CBR) system whose knowledge is encoded in a semantic wiki. Automatic processes like the CBR reasoning process itself, or specific tools for acquiring AK are integrated as wiki extensions. These tools and processes are combined on purpose to collect AK. Users are at the center of our approach, as they are in a classical wiki, but they will now benefit from automatic tools for helping them to feed the wiki. In particular, the CBR system, which is currently only a consumer for the knowledge encoded in the semantic wiki, will also be used for producing knowledge for the wiki. A use case in the domain of cooking is given to exemplify the man-machine collaboration.

TAAABLE : un système de raisonnement à partir de cas qui adapte des recettes de cuisine

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, and succeeds thanks to a smart combination of various knowledge-based system methods and techniques, such as: CBR, knowledge representation, acquisition, discovery and management, and natural language processing. In this paper, we first present the CBR engine and its features such as the retrieval process based on minimal generalization of a query and different adaptation processes. Then the knowledge containers used by the system, and how they were acquired is detailed. To conclude, we discuss some research developments resulting from this application.