International audience

Crisis situations can present complex and multi-faceted challenges, often requiring the involvement of multiple organizations and stakeholders with varying areas of expertise, responsibilities, and resources. Acquiring accurate and timely information about impacted areas is crucial to effectively respond to these crises. In this paper, we investigate how collaborative and social technologies help to contextualize crises, including identifying impacted areas and real-time needs. To this end, we define CORec-Cri (Contextulized Ontology-based Recommender system for crisis management) based on existing work. Our motivation for this approach is two-fold: first, effective collaboration among stakeholders is essential for efficient and coordinated crisis response; second, social computing facilitates interaction, information flow, and collaboration among stakeholders. We detail the key components of our system design, highlighting its potential to support decision-making, resource allocation, and communication among stakeholders. Finally, we provide examples of how our system can be applied to contextualize crises to improve crisis management.

Online learning provides learners with appropriate learning environments and a large amount of various educational resources, while eliminating the constraints of time and space. How to optimize the learning experience for learners has become a top priority. We focus on helping learners choose suitable resources, maintaining their enthusiasm for learning, and preventing them from dropping out. In this paper, we propose a complete solution to address the above challenges. First, we design a semantic online learning environment based on resource-sharing, where learners can perform a variety of learning-related activities (e.g., share, access, and vote), and learners’ processes are described by knowledge graph. Then, we derive learners’ features from their activity records collected by web logs, and form learners’ feature models. Finally, we combine knowledge based embedding with collaborative learning to provide personalized recommendation services for learners.

Existing recommender systems often apply factorization-based models, which have been proved to be efficient in rating prediction. However, the explicit semantics of the learned latent factors are not clear, which makes it difficult to explain the recommendations returned. In another line of research, argumentation-based methods have become an important tool in explainable artificial intelligence. In this work, we propose an Attribute-Aware Argumentative Recommender (A3R) that combines factorization-based methods and argumentation. With the help of argumentation framework, each step of A3R is endowed with explicit semantics, enabling A3R to generate easily understandable explanations for recommendations. Experiments on five datasets from three different domains (movie, music, and book) show that A3R can achieve competitive rating prediction when compared with factorization-based methods; A3R can largely improve prediction accuracy when compared with state-of-art argumentative recommendation methods.

Nowadays, the number of collaborative tools has increased significantly. This makes it difficult for users to find the collaborators that are most relevant to their needs among these tools. Besides, their needs can also be influenced by the context of collaboration (e.g., workplace, tools, and resources). This raises an issue: how to help users find their collaborators within the collaboration context. In our research, we propose an ontology-based semantic similarity and employ it in a collaboration context ontology to generate context-aware collaborator recommendations for users. In this paper, we present how to calculate and apply the semantic similarity in context-aware recommendation algorithms.

Learning with huge amount of online educational resources is challenging, especially when variety resources come from different online systems. Recommender systems are used to help learners obtain appropriate resources efficiently in online learning. To improve the performance of recommender system, more and more learner’s attributes (e.g. learning style, learning ability, knowledge level, etc.) have been considered. We are committed to proposing a learner-based recommender system, not just consider learner’s physical features, but also learner’s mood while learning. This recommender system can make recommendations according to the links between learners, and can change the recommendation strategy as learner’s mood changes, which will have a certain improvement in recommendation accuracy and makes recommended results more reasonable and interpretable.

Integrating various collaborative tools, a web-based Collaborative Working Environment can support collaborations between users. In collaborative processes, many resources are produced and stored distributively within these tools. This raises an issue: how to organize these resources in a Collaborative Working Environment. In our research, we intend to consider a Collaborative Working Environment as an ontology-based collaborative System of Information Systems and apply a collaboration context ontology for managing resources and generating resource recommendations to users. In this paper, we present a prototype of such environments and show how it can be used.

To improve the performance of recommender system, more and more learner's attributes (e.g. learning style, learning ability, knowledge level, etc.) have been considered. But it has also triggered widespread privacy concerns due to their reliance on learner's personal information. Therefore, towards the privacy-preserving of online recommender system in collaborative learning environment, we propose a personalized recommender system with three customized settings about recording (full-collecting mode, semi-privacy mode, full-privacy mode) to collect learner's history study activities. We aim at extracting learner information from these activity records to build recommender system, which can not only make effective personalized recommendations but also meet privacy-preserving requirements.

Web-based Collaborative Working Environments intend to support collaborations between users by integrating and offering different collaborative tools. However, this makes it difficult for users to manage and find useful resources to advance their collaborations, which are stored distributively within these tools. This raises an issue: how to manage these resources and identify useful ones for users. In our research, we intend to consider a web-based Collaborative Working Environment as an ontology-based System of Information Systems and to apply a collaboration context ontology that can manage and recommend resources to users within the context of collaboration. In this paper, we present a prototype of such environments and show how context-aware resource recommendations can be generated.

Collaboration occurs almost everywhere. The challenge today is how to succeed it. In addition, the development of digital technologies requires higher demands to succeed in collaborations and thus makes the challenge more difficult to handle. To address it properly, we study impacting factors that affect the success of collaborations and integrate them into collaboration context ontology to analyze and evaluate the success of collaborations supported by digital technologies. In this article, we present the collaboration context ontology that we have developed and show why and how it can be used.

Working in groups aims to exceed the result that could be obtained by the simple sum of results achieved individually. To this end, the definition of the group is a key element: How to choose the members of the latter, on what criteria to identify them? In our work we focus on the process of forming groups of learners taking into account the characteristics of learners and more generally the context in which they evolve. We specify what we mean by context before presenting our context-based decision support to form relevant groups of learners. Using a motivating example, we illustrate how to use the multi-criteria classification process for decision support. We then discuss its applicability within a collaboration platform developed from a semantic model.

Warnings can help to prevent damage and harm if they are issued timely and provide information that helps respondents and population to adequately prepare for the disaster to come. Today, many indicators and sensor systems are designed to produce alert and reduce disaster risks. These systems have proved to be effective but they remain complex, include different expertise components, and are difficult to manage. We study in this paper the case of the National Early-Warning System in France (called SAIP), which can be seen as a System of Systems (SoS). A lot of SoSs exist. They can be directed, collaborative, virtual or even acknowledged systems. We study here what type of system corresponds to the French Early-Warning System, which openings may reasonably be considered for this system and we introduce a new category of SoSs: “delimited systems”.

Group work, in certain circumstances, could encourage peer learning and provide the learners an opportunity to clarify and refine their knowledge. However, randomly grouping learners of different level of knowledge and activeness could decrease the effectiveness of a group. In this paper, we present a criterion for recommending the formation of groups of learners. This criterion is based on traces collected from interactive digital platforms. Traces are then processed with Bayes Classifier. We implemented this prototype using the MEMORAe approach.

With the current development of Information and Communication Technologies (ICT), organizations deal with great amount of information coming from many systems. Identifying a resource of relevant information to a specific context becomes a real challenge. In the course of our work we are interested in recommendation of pedagogical resources within a learning ecosystem. We have chosen to model a learning ecosystem as a system of information systems (SoIS). In this SoIS we introduce a resource recommender system. This system is based on users' votes (learners, teachers), and the similarity between the description of pedagogical resources and the learners' profile. In our approach, we take into account the willingness to collaborate. Therefore, we exploit a collaboration model that supports learning ecosystem to answer the demand for questions such as, who collaborates with whom, how, when, why, on what and where, etc. The work presented in this paper is focused on at recommendation of pedagogical resources within a learning ecosystem.

Community-based Question Answering (CQA) services are becoming popular as the public gets used to look for help and obtain information. Existing CQA services try to recommend someone for answering new questions. On the other hand, people are allowed to exchange information and experience using various collaborative tools. It would be interesting to combine the two approaches to increase the reliability of recommending an answerer. Thus, relying on semantically modeled traces, we propose a comprehensive approach that recommends an answerer in a collaborative environment. From a global point of view, this approach consists in evaluating users by the performance in the CQA services and the corresponding knowledge sharing activities in which they participated in a collaborative context. By modeling and analyzing users' behavior, we assess the competency of an answerer in a particular collaborative context.

Warnings can help prevent damage and harm if they are issued timely and provide information that help responders and population to adequately prepare for the disaster to come. Today, there are many indicator and sensor systems that are designed to reduce disaster risks, or issue early-warnings. In a socially and environmentally responsible word, we need effective Early-Warning Systems (EWS). EWS are Information and Knowledge Systems dedicated to protect people against disasters damages. Such systems are designed to integrate data, information and knowledge from various sources and actors who do not usually interact to issue early-warnings. This paper introduces knowledge implications in EWS decision support design in general, with a discussion on communication processes between data, information and knowledge. We propose a knowledge-oriented vision of EWS elements to examine existing systems and provide dynamic and flow-oriented models. In this perspective, we analyze knowledge integration processes in the design of the fire safety system of our University.

In order to achieve individual or collective goals, users in informational environments collaborate to integrate intellectual resources and knowledge. Thanks to informational environments, users can better organize, realize and record collaboration. Every activity produces a set of traces. Such traces can be recorded and classified, based on a model of traces. With the help of a model of competency, these traces also contribute to evaluate the competency of users on certain subjects. In this article, we propose a semantic model of traces and analyze classified traces by means of TF-IDF. We also considered the impact of time on the decreasing importance of traces. We show how to offer users recommendations and decision aid.

These traces in return offer a clue whether a user is competent enough on a subject. This helps further collaboration because knowing the specialization of users helps to distribute tasks reasonably. In this article, we propose a semantic model of traces and analyze classified traces using a Bayes classifier. We exploit the results to offer recommendation on competent users accordingly.

This paper introduces a formalization of the Data, Information, Tacit and Explicit Knowledge process (DITEK) empirical model. This formalization reduces the gap between empirical and formal worlds, what led us to consider it as an opportunity to enhance the communication between users and designers of digital information systems. Notably, the designer will be aware of the role of the user as a component of the Enterprise's Information and Knowledge System (EIKS) defined in this paper. We highlight how having two models, empirical and formal, for the same concept can constitute an outline in order (i) to enhance the communication between users and designers of digital information systems, (ii) to regard users as components of the EIKS, and (iii) to promote information systems' innovative design.

Data warehouses store large volumes of consolidated and historized multidimensional data for analysis and exploration by decision-makers. Exploring data is an incremental OLAP (On-Line Analytical Processing) query process for searching relevant information in a dataset. In order to ease user exploration, recommender systems are used. However when facing a new system, such recommendations do not operate anymore. This is known as the cold-start problem. In this paper, we provide recommendations to the user while facing this cold-start problem in a new system. This is done by patternizing OLAP queries. Our process is composed of four steps: patternizing queries, predicting candidate operations, computing candidate recommendations and ranking these recommendations.

Social networks are dynamic structures in which entities and links appear and disappear for different reasons. Starting from the observation that each entity has a more or less important role within the network, the objective of this article is to propose a method which exploits the role played by nodes to predict the new structure of a social network once one entity disappears. The role of a node in the network is expressed in terms of the number of interactions it has with the rest of the network. Two roles are considered: the leader and the mediator with their corresponding measure: the degree centrality and the betweenness centrality.

Recommending database queries is an emerging and promising field of investigation. This is of particular interest in the domain of OLAP systems where the user is left with the tedious process of navigating large datacubes. In this paper we present a framework for a recommender system for OLAP users, that leverages former users' investigations to enhance discovery driven analysis. The main idea is to recommend to the user the discoveries detected in those former sessions that investigated the same unexpected data as the current session.

An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.