This paper provides insight into how gaming can be used to explore the children’s experience of food, and to envision possible future transitions in their eating habits. The case study presented is part of the ERMES project, an interdisciplinary initiative funded by the French National Research Agency (ANR-23-CE36-0009), focused on primary school children aged 9–10 in the Île-de-France region. The project aims to develop scientific understanding on how children engage with and relay “food messages” in their daily lives, and how these messages influence their attitudes and eating habits. The paper presents and discusses the use of research game-based workshops to capture children’s attitudes, preferences, and understanding of nutrition in a playful and engaging manner. These activities not only facilitate data collection but also foster trust and active participation among children. Additionally, the case study highlights the challenges of conducting action-oriented research in school settings, including minimizing researcher influence and addressing scientific bias. Findings suggest that game-based approaches are effective in exploring complex topics like sustainable eating, offering insights into children’s peer culture and food-related behaviors. However, the study emphasizes that game-based activities should complement, rather than replace, traditional methods such as observations and surveys. By integrating games into research, the study provides a novel framework for understanding children’s food perceptions, contributing to the development of effective food education strategies.

In recent years, keen interest towards Knowledge Graphs has increased in both academia and the industry which has led to the creation of various datasets and the development of different research topics. In this paper, we present an approach that discovers differential causal rules in Knowledge Graphs. Such rules express that for two different class instances, a different treatment leads to different outcomes. Discovering causal rules is often the key of experiments, independently of their domain. The proposed approach is based on semantic matching relying on community detection and strata that can be defined as complex sub-classes. An experimental evaluation on two datasets shows that such mined rules can help gain insights into various domains.

This paper deals with redundant data, previously identified by a data linkage step. The question considered is: how to propose a unique representation by merging the identified "duplicates"? More specifically, how to decide, for each data property, which value will be chosen among those describing the "duplicates"? What method should be adopted in order to be able to trace and explain the result to the user in an understandable form? The proposed approach relies both on multi-criteria decision and argumentation, combined with the computation of a quality score.

This paper deals with the process of decision making in the reverse engineering mode and highlights the need for polyvalent information. Three aspects are considered. 1) Reverse engineering implies a preliminary assumption: having defined a desired outcome of the decision process. Defining goals on the possible outcomes is a complex, multi-actor process based on ubiquitous information. Once identified at best, several alternative scenarios may lead to the desired outcome. The first issue consists in evaluating these alternative scenarios. 2) While taking into consideration the positive consequences that the different alternatives will generate, the decision process has to allow for possible negative impacts, which are not explicitly expressed in the defined goals. We thus consider the reverse engineering process has to be bipolar and take rejections into account. 3) Finally, the simultaneous achievement (respectively, avoidance) of several goals (respectively, rejections) is not always possible and depends, in particular, on whether the actions leading to each of these goals (respectively avoiding these rejections) are compatible or not. We thus seek the “best” compatible set of actions and propose to define it as optimizing the bipolar preferences expressed on the outcomes. The approach is both graphical and logical and is focused on a case study in breadmaking technology.

This paper presents a new type of fuzzy sets, called "hierarchical fuzzy sets", that apply when the considered domain of values is not "flat," but contains values that are more specific than others according to the "kind of" relation. We study the properties of such fuzzy sets, that can be defined in a short way on a part of the hierarchy, or exhaustively (by their "closure") on the whole hierarchy. We show that hierarchical fuzzy sets form equivalence classes in regard to their closures and that each class has a particular representative called "minimal fuzzy set". We propose a use of this minimal fuzzy set for query enlargement purposes and, thus, present a methodology for hierarchical fuzzy set generalization. We finally present an experimental evaluation of the theoretical results described in the paper, in a practical application

In this paper, we present a new method, called multiview fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software. MIEL is used to query the Sym'Previus database which gathers information about the behavior of pathogenic germs in food products. In this database, data are incomplete because information about all possible food products and all possible germs is not available; data are heterogeneous because they come from various sources (scientific bibliography, industrial data, etc); data may be imprecise because of the complexity of the underlying biological processes that are involved. To deal with heterogeneity, MIEL queries the database through several views simultaneously. To query incomplete data, MIEL proposes to use a fuzzy set, expressing the query preferences of the user. Fuzzy sets may be defined on a hierarchized domain of values, called an ontology (values of the domain are connected using the a kind of semantic link). MIEL also proposes two optional functionalities to help the user query the database: i) MIEL can use the ontology to enlarge the querying in order to retrieve the nearest data corresponding to the selection criteria; and ii) MIEL proposes fuzzy completion rules to help the user formulate his/her query. To query imprecise data stored in the database with fuzzy selection criteria, MIEL uses fuzzy pattern matching.