This paper investigates the use of digital collaborative tools and live streaming systems to support student initiation into scientific research. A Computer-Supported Cooperative Work in Design (CSCWD) system was implemented to aid two key phases: collaborative planning in a digital workspace and experimental execution in laboratories. In the first phase, multitouch surfaces enabled students to collaboratively define research problems and protocols, fostering idea generation and consensus building. The second phase utilized video capture and streaming systems for documentation and remote participation in experiments. While these tools showed potential, challenges such as camera resolution, latency, and feedback limitations emerged. Initial findings suggest these technologies support research skill development, but further improvements are needed to enhance usability and engagement for both local and remote participants. Future work will focus on addressing these technical and interaction challenges to better support hybrid learning environments.

Gamified e-learning systems aim to enhance learning outcomes and foster learner's engagement by incorporating multiple game elements. These elements have varying influence on the learner's behavior, such as encouraging focus, competition, or exploratory attitudes. However, game elements are often implemented within the learning process in a uniform way that is not adaptable, with no available metadata about them, and without the teacher's involvement or choice. However, since game elements have a behavioral effect on the learners, the selection of relevant game elements should include the teachers' objectives for effective gamification. Therefore, this paper proposes an approach to facilitate understanding and formalizing gamification concepts for enhanced understanding from the teacher's side so that they can apply it and share it. The proposed approach begins with defining gamification and its associated properties and how it can be formalized in a semantic model. This ontological model is then populated through data collection and integration processes, which is then validated by means of SPARQL queries. This approach lays the foundation for future advancements, including the potential to recommend appropriate teachers to assist another in integrating gamification into e-learning processes.

Group recommender systems aim to generate recommendations that align with the collective preferences of a group, introducing challenges that differ significantly from those in individual recommendation scenarios. This paper presents Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning, a framework that unifies group profiling and recommendation tasks within a single model. By jointly learning these tasks, the model develops a deeper understanding of group dynamics, leading to improved recommendation accuracy. The shared representations between the two tasks facilitate the discovery of latent features essential to both, resulting in richer and more informative group embeddings. To further enhance performance, an attention mechanism is integrated to dynamically evaluate the relevance of different group features and item attributes, ensuring the model prioritizes the most impactful information. Experiments and evaluations on real-world datasets demonstrate that our multi-task learning approach consistently outperforms baseline models in terms of accuracy, validating its effectiveness and robustness.

In the contemporary educational context, teachers are confronted with the challenge of efficiently managing and utilizing a plethora of heterogeneous pedagogical resources across multiple digital platforms and sources, while simultaneously fostering collaboration in a dynamic educational collaborative environment. This challenge is further amplified by the increasing reliance on digital tools, which often operate in isolation, thereby limiting effective resource sharing and retrieval. To address these challenges, this paper explores the integration of a context-aware pedagogical resources recommender system for teachers within an educational collaborative environment. The paper begins with an identification of the necessity for an integrated approach that connects multiple information systems in order to support resource management and enhance collaborative practices. These are established as a foundation for integration within MEMORAe, a collaborative system of information systems, which employs ontologies for the organisation of heterogeneous resources from different systems and leverages collaborative knowledge sharing. Our integration approach employs the use of MEMORAe to address three pivotal challenges: the structuring of resources, the utilization of context information and the facilitation of collaboration.

Prerequisite skills - foundational competencies required before mastering more advanced concepts - are important for supporting effective learning, assessment, and skill-gap analysis. Traditionally curated by domain experts, these relationships are costly to maintain and difficult to scale. This paper investigates whether large language models (LLMs) can predict prerequisite skills in a zero-shot setting, using only natural language descriptions and without task-specific fine-tuning. We introduce ESCO-PrereqSkill, a benchmark dataset constructed from the ESCO taxonomy, comprising 3,196 skills and their expert-defined prerequisite links. Using a standardized prompting strategy, we evaluate 13 state-of-the-art LLMs, including GPT-4, Claude 3, Gemini, LLaMA 4, Qwen2, and DeepSeek, across semantic similarity, BERTScore, and inference latency. Our results show that models such as LLaMA4-Maverick, Claude-3-7-Sonnet, and Qwen2-72B generate predictions that closely align with expert ground truth, demonstrating strong semantic reasoning without supervision. These findings highlight the potential of LLMs to support scalable prerequisite skill modeling for applications in personalized learning, intelligent tutoring, and skill-based recommender systems.

Measuring similarity between RDF graphs is essential for various applications, including knowledge discovery, semantic web analysis, and recommender systems. However, traditional similarity measures often treat all properties equally, potentially overlooking the varying importance of different properties in different contexts. Consequently, exploring weighted property approaches for RDF graph similarity measure presents an intriguing avenue for investigation. Therefore, in this paper, we propose a weighted property approach for RDF graph similarity measure to address this limitation. Our approach incorporates the relative importance of properties into the similarity calculation, enabling a more nuanced and context-aware measures of similarity. We evaluate our approach through a comprehensive experimental study on an RDF graph dataset in the vehicle domain. Our results demonstrate that the proposed approach achieves promising accuracy and effectively reflects the perceived similarity between RDF graphs.

Gamification in e-learning systems implies integrating game elements in non-gaming context to attain the learner’s engagement through pre-defined engagement objectives. These objectives impact the selection of the to-be-applied game elements, which typically influences the learner’s behavior within the learning process. In turn, this behavior is expected to help the teachers in reaching their learning objectives. Therefore, involving teachers in the gamification process as they are in the learning process can ensure the coherence between the learning and engagement objectives. However, since teachers often lack familiarity with gamification, they are rarely involved with the gamification process. Accordingly, recommender systems can play a vital role in assisting teachers through gamification. Therefore, this paper investigates the existing recommender systems from teacher’s perspective to highlight the advancements proposed for assisting teachers and examines the utilization of game elements within gamified e-learning systems to identify their types and explore their practical applications. Based on our research results, this paper proposes a (1) game elements categorization based on their practical applications and (2) a layered recommendation process flow that incorporates the different game elements categories, to assist the teachers in gamifying the learning process.

Ethics, defined initially as moral and virtues principles, becomes more and more deontological and social rules, especially to evaluate development and use of AI systems. In this paper, we present investigations to show the importance of ethics in AI technologies emphasizing their important impact in society and research.

Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.

International audience

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. Embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector.

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.

The current recommender systems’ approaches favor item’s recommendation over peer’s recommendation. Such virtual information systems do not allow face-to-face communication for collaborative knowledge exchange between peers. However, the on-going worldwide situation affects the sentimental state and especially for a teacher who struggle to adapt his course’s contents according to current variables. These challenges can be mitigated through personalized peer recommendations for teachers. A more-experienced teacher can provide the required support for a less-experienced one in the form of knowledge sharing. This paper proposes a matching approach to provide peer recommendations for teachers in consonance with their context and sentimental state. The peer recommendation aids the teacher to collaborate with other teachers in the form of experience sharing and knowledge sharing. Furthermore, the paper highlights the similarity measurement criteria for contextual and sentimental matching algorithm in addition to predefined rules. At the end, the paper discusses the effectiveness of the algorithm application with a real-life scenario.

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.

International audience

Many studies faced the problem of vehicle autonomous navigation in different fields, but nowadays just a few of them uses all the implicit information coming from the context in which such navigation is occurring. This results in a huge potential information loss that prevents us from adapting the vehicle’s behavior to each different situation it may be in. In a previous work, we defined a method to model the static context of navigation using ontologies and take it into account in the command law when performing a local navigation task. In this paper, we extend our model of the context of navigation, and define a software architecture able to update the context dynamically, by using sensor information. The method is tested with real-time experiments on driving simulator. They show that the Context of Navigation can be effectively updated during the navigation and leads to a smarter vehicle’s behavior on the road.

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.

Most of the time, teachers have to search an enormous amount of unorganized resources in order to find the one that suits their cause. Thus, the process of providing innovative resources to support teachers, is becoming one of the essential keys to guarantee a successful learning process for students. Furthermore, this process of providing resources must consider multiple criteria including teacher's experience, students' level of education, cultural background, teaching style and various learning styles. As a result, we find ourselves in front of a more complex problem which needs standardization of resources' description. This unified description of resources can be achieved through a well-represented ontology that strengthens the knowledge-sharing value between teachers granting resources delivery for the intended teacher. This paper introduces a teacher context ontology (TCO) which can be used to organize the knowledge-sharing between teachers through an educational resources recommender system. The ontology contains multiple concepts; teacher profile, resource, targeted audience, and the context in which the teacher uses this resource. MEMORAe is a platform which provides knowledge organization in a collaborative environment. Therefore, TCO benefits from MEMORAe to organize the knowledge-sharing of experiences and resources between teachers.

Recommender systems in education improve the teacher’s working process by providing relevant resources to aid his course design in addition to learning new teaching methodologies. However, these systems have limited adaptability according to a global evaluation of teacher’s activities. This approach of user profiling is convenient, but not adequate for teacher’s context description. In our approach, it is assumed that the utilization of teacher’s emotions has an inevitable role to accomplish a full contextual description for teacher. Teacher context ontology (TCO) provides a representation for the teacher’s living and working contexts along with the main educational concepts. In this paper, we introduce a conceptual integration approach between Moodflow@doubleYou emotional data as a concept and TCO ontology. Furthermore, we intend to prove the importance of integrating such concept for sufficient teacher’s context description. The impact of utilization emotional data in educational recommender systems is discussed. Finally, this paper represents the conducted experiments’ results which show the advantage of such integration.

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.

This paper faces the problem of intelligent vehicles in interaction with their occupants and the environment, by modelling the semantic context associated to the navigation. With a semantically modelled context, an intelligent vehicle will not only drive itself safely, but it will also be able to reason on the situation and act accordingly. To do so, it is necessary to first define the Context of Navigation, and then to set the inference rules for it, in order to enrich the robot’s comprehension of the situation. In this paper we propose our definition of the Context of Navigation, based on the information that could be needed by the vehicle’s controller. We split it into two components: the Dynamic Context and the Static Context. In this paper we will focus on the latter. We then model the Static Context of Navigation of the autonomous driving - for instance the passengers’ driving preferences - and to make the robotic car adapt its behaviour to this new information in real time. Finally, a short practical example of our proposition is shown and discussed.

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.

Cooperation is a fundamental trait of Systems of Systems (SoS). In this paper we discuss the design and implementation of proactive vocal interfaces with the goal of improving and stimulating cooperation between users and constituent systems. Our approach is based on intelligent personal assistants displaying proactive behavior and capable of interacting vocally. The paper discusses the main challenges for integrating proactive behavior into SoS interfaces, as well as for using them. The proposed approach is generic and can be applied to different domains.

Despite the great number of Systems of Systems (SoS) being developed, building them still remains hard and difficult. Currently, there is a lack of methods capable of supporting architects for building an actual SoS. In this paper we introduce an original method called GAMBAD for developing an SoS from a practical point of view. Our method guides the development of SoS on top of a multi-agent layer supported by ontologies. We tested GAMBAD by building an SoS in the domain of Health Care. Early results show that by using our method, architects can develop an SoS faster and more accurately.

Working with Information and Communications Technologies (ICT) is challenging, especially for learners, as ICT control most of their work environment. These learners need to be supported with solutions that are up-to-date with the technological challenges, and can keep up with the fast-paced evolving ICT. Acquiring new knowledge is important for the progress of learners, but it is getting harder to obtain with the overwhelming amount of resources produced by many Information Systems (ISs) in the learning environment. In order to help learners manage resources, we are following the approach of System of Information Systems (SoIS) to support learners. The SoIS gives the opportunity to explore how individual and organisational learning can be enhanced through a combination of different ISs in the learning environment. In this context, we look into the orchestration of SoIS that can provide the ability to index, share, annotate, and recommend important resources in the learning environment. In that sense, this paper presents the approach of SoIS to support learners with a case study at the University of Technology of Compiègne.

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.

Good Decision Support Systems require three main features: (i) a good handling of the domain data and information; (ii) an efficient user interface; and (iii) a good knowledge of past decisions. Usually such features are handled by different specialized systems difficult to integrate. In this research we keep specialized systems independent, focusing on interoperability. We propose a system of systems architecture (SoS) integrating a domain system in which users interact, a multi-agent system implementing an efficient user interface and taking into account results from the domain system, and a platform to capitalize and manage knowledge. Our approach extracts indicators from the interaction or behavior of users within the domain system, and provides them analyses, statistics and recommendations to help them reach good decisions. We built a prototype and applied it to two different domains: collaborative software development and healthcare. In this paper, we will focus on the multi-agent system which is a key component of the SoS architecture.

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.

Organizations struggle to manage overwhelming amount of heterogeneous resources, which are produced by several users working on different projects using various Information Systems. This paper takes the task for modeling and developing a System of Information Systems (SoIS) for managing heterogeneous resource. In order to manage these resources we are following the approach of SoIS that can be viewed as a single entry point for several Information Systems granting the user access to information and resources produced from multiple autonomous Information Systems. It can provide the ability to capitalize the knowledge of users as they are interacting with Information Systems and each other. In this context, we look into the ability to index, share, and annotate important resources in the environment of the SoIS. In that sense, this paper focuses on modeling and developing a System of Information Systems (SoIS) for managing heterogeneous resource.

In a System of Systems (SoS), heterogeneous systems cooperate to achieve a higher purpose. Developing an SoS is complex and difficult, in particular interfacing systems coherently to make them interoperable. In this research, we propose a core architecture to simplify the development of SoS. Our approach is based on a model we call MBA: "Memory Broker Agent," consisting of agents for encapsulating systems, brokers for handling requests for functionalities and results, and memories for capitalizing and managing knowledge. In this paper we focus on the communication protocol of the proposed architecture, which provides semantic and multilingual interoperability, facilitating the exchange of information and the collaboration between system users. We have built a prototype implementing our approach and applied it to a case study in the health care domain.

Digital Ecosystem (DE) is a concept emerged from the natural existence of business ecosystem, which in turn is taken from the concept of biological ecological systems. On the other hand, System of Information Systems (SoIS) are special type of System of Systems (SoS) that deals with several Information Systems producing overwhelming amount of information. In this paper we aim to define the concepts of DE, SoS, and SoIS. Then, provide the guidance for moving from Digital Ecosystem to System of Information Systems. Thus, we aim to draw the link between the model of DE presented in literature and the concept of SoS and SoIS. Then, we propose an architectural model of System of Information Systems (SoIS) and investigate the knowledge base role in such complex system operating in the environment of a Digital Ecosystem. These complex Digital Ecosystems pose a significant technical improvement in terms of information interoperability that overcomes conceptual and technical barriers. In this paper we are moving from Digital Ecosystem to System of Information Systems by defining the similarities between the two concepts, then proposing an architectural model of SoIS in correspondence to DE model.

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.

Collaborative software development is a complex activity. An important factor that needs to receive attention in collaborative software development is software quality. High quality software reduces the development and the maintenance; improves delivery schedules; and reduces repairs and rework. In order to measure, evaluate, control and improve the software quality, software metrics can be used. In this research we present an advanced collaborative environment for software development currently being built, called ACE4SD, which intends to support the improvement of the code quality during collaborative software development. ACE4SD is a system of systems composed of a software development environment, a multi-agent system and a platform to capitalize and manage knowledge, all of them being integrated in the same environment. ACE4SD can provide personalized support to team members to improve the code quality and encourage its reuse, it can answer questions or doubts arisen during the development, record document problems and solutions, and improve the awareness and collaboration between the participants.

In the present work, we presented a new approach to provide learners with learning paths adapted to their requests. These courses were generated by the composition of the learning semantic Web services. Our approach defined a learning Web service (WS) for each learning object (LO) to overcome the problems of interoperability and accessibility of learning objects. Each WS was represented, in the directory, by a learning semantic Web service (OWLS-LO) to describe a semantic understanding of the object being represented. This semantic approach was described by ontologies.

The article introduces the operational and theoretical challenges associated with the ECOPACK project that targets the development on digital collaboration platforms in order to facilitate an expert group. A return on experience with expert groups run with different modalities has made it possible to understand that the challenge locates at the level of the emergence of “weak ties” within the expert group. The digital platform also requires the monitoring of collaboration traces. It has to be considered as a “boundary object”.

Digital ecosystem is a concept emerged from the natural existence of business ecosystem, which in turn is taken from the concept of biological ecological systems. In this paper we aim to review the literature in the field of digital ecosystem and investigate models that support the claim that collaboration systems and social media networks, to certain extent, could be considered as digital ecosystem. For this investigation we undertake a systematic mapping study to present a wide review of primary studies on digital ecosystems, collaboration systems, and social media networks. The systematic mapping is a research methodology that gives a visual summary of the results obtained from a systematic research process. This paper mapped what is currently known about digital ecosystem. The work presented in this paper aims at investigating the similarity between collaboration systems and digital ecosystems and guide efforts for future research intended to model collaboration system as a digital ecosystem. Finally, we present our own model of a collaboration system working as a digital ecosystem based on the knowledge obtained from our investigation.

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.

Social network sites with large-scale public networks like Facebook, Twitter or LinkedIn have become a very important part of our daily life. Users are increasingly connected to these services for publishing and sharing information and contents with others. Social network sites have therefore become a powerful source of contents of interest, part of which may fall into the scope of interests of a given group. So far, no efficient solution has been proposed for a group of interest to tap into social data, especially when they are protected by and scattered across different social network sites. We have therefore proposed a user-centered approach for integrating social data into groups of interests. This approach makes it possible to aggregate social data of the group's members and extract from these data the information relevant to the group's topic of interests. Moreover, it follows a user-centered design allowing each member to personalize his/her sharing settings and interests within their respective groups. We describe in this paper the conceptual and technical components of the proposed approach. To illustrate further the approach, a web-based prototype is also presented. A preliminary test using this prototype was carried out and showed encouraging results.

Any activity in a computer-supported cooperative working environment produces a set of traces. In a collaborative working context, such traces may be very voluminous and heterogeneous. They reflect all the interactive actions among the actors themselves and between the actors and the system. This paper examines what is required to exploit traces in the context of a collaborative working environment. The objective is to propose a model abstracting the exploiting process from a set of collaborative traces, according to a goal and a specific target framework. The example of the SWOT analysis is described to illustrate the approach.

The wide adoption of social and connected tools in organizations lead them to think again about their behavior and to consider the indexing of the resources users can now produce with such tools, in the same way they index other documentary resources. This indexing process has to be done along the core frame of reference of the organization. We present in this paper the model of a system which permit the indexing with the help of an ontology of both documentary resources or those produced on a social platform resources.

Online social networks (OSNs) such as Facebook, Twitter, Linked In, Google+, have become extremely popular and ubiquitous today. Users are actively connected to these services for creating and sharing contents and events with others, and, in some cases, this activity takes place in the scope of groups of interests. Therefore, from amongst the morass of data generated every day by users, a part of information may match the interests of certain groups. In practice, members are not all linked to each other via each OSN they are connected to. It is also not realistic to assume that each member can manually explore all others' social profiles to reach the information that may be relevant to their interests. Thus, there is a need for aggregating members' social streams on a single information support to collect relevant information, and, consequently, to promote collaborative knowledge-sharing. However, the disconnected nature of today social websites prevents a straightforward aggregation process. An efficient automated aggregation model is needed. We present, in this paper, the idea of empowering collaborative intelligence by the use of a user-centered approach for OSN aggregation. We illustrate the approach by a first experience to evaluate its impact on users information sharing and enrichment capabilities.

Any activity in a computer-supported cooperative working environment produces a set of traces. In a collaborative working context, such traces may be very voluminous and heterogeneous. They reflect all the interactive actions among the actors themselves and between the actors and the system. This paper examines what is required to study traces in the context of a collaborative working environment (CWE). The objective is to propose a definition of the different kinds of traces and to build a model for classifying and analyzing the interactions with respect to both individual needs and group needs. In a CWE the different types of traces can be divided into two categories: Collaborative Traces and Private Traces. As a basis of our model, the Collaborative Trace represents the shared personal experiences in a group and can be filtered to improve the collaborative and personal work. For practical applications, we validate our model using the E-MEMORAe2.0 platform.

Current economical context and industrialization accelerate product development which becomes more and more complex, in particular for complex systems such as railway systems. Consequently, project organization has also an increasing complexity which is reflected in the number of collaborators grouped into teams, in the heterogeneity of the competences and skills as well as the distribution of these teams in different places around the world. It is also the case of developing a customer solution in railways transport which is made possible due to a tight collaboration between the stakeholders in order to accomplish critical activities such as: the understanding of the customer's real needs and the design of railway systems. Furthermore, diversity in customer background adds further complexity to this process. Therefore, there is a need to find improved ways of supporting activities during customer solution development. A study carried out in the company A revealed there was a need to support customer solution development during Tendering (an early phase of product development). In this context, our goal is to build a dedicated workspace to support collaboration and re-use of past individual and collective knowledge. The paper presents a model and a first implementation of the workspace.

Product-line development in railway context is challenged by the heterogeneity of the railway market, as a consequence of the fact that the reference solution cannot perfectly match each customer demand. Therefore, a process of further adapting the reference solution is necessary and this adaptation starts with the Tendering phase which thus becomes one of the most critical of a product life-cycle. A study carried out in the company A revealed that knowledge management could bring further improvements to the Tendering, in particular as regard to knowledge primordial for this process, which we generically call “intangibles” and to knowledge sharing. In this context, we think that knowledge management should be joined to organizational learning to support the Tendering process. Furthermore, we want to build a dedicated workspace to respond to these needs. In this paper we explain how knowledge management can contribute to the enhancement of the Tendering process in the company A and we propose an approach to support knowledge management and organizational learning through a dedicated workspace.

In the current economic environment, to learn became the best means, for an organization, to be competitive in preserving knowledge and experiments of each collaborator and each team. This leads companies to reconsider their knowledge sharing, to anticipate their members' training, while managing the changes induced by new practices. Such companies are called learning organizations because they build structures and strategies in order to increase and to maximize the organizational learning. In this paper we present why and how we associated on the one hand knowledge and learning engineering methods and on the other hand Semantic Web and Web2.0 technologies to develop a collaborative learning environment E-MEMORAe2.0 for organizational learning support.

Current economical context and industrialization accelerate product development and put forward automation. Still, product development requires also human intervention in a number of activities which cannot be entirely achieved by machines. It is the case of product development for railway markets, where the design of railway systems is guided by the evolving needs of the customers. Therefore, there is a need to locate and find ways of fostering tacit knowledge involved in these activities in order to improve, in time, railway product development. A study carried out in the company A revealed there was a need to support system design during Tendering (an early phase of product development). In this context, we think the problem of tacit knowledge loss is a concern of organizational learning and we want to build a dedicated workspace to overcome these drawbacks. In this paper we present the modeling and the architecture of the proposed workspace.

Knowledge and competences capital of an organization is increasingly crucial. The organization survival depends mainly on its capacity to access new knowledge, to diffuse its competences quickly and to exploit and preserve its expertise fields efficiently and durably. Thus today, organizations are aware of the necessity to become learning organizations and to maximize organizational learning. Within the approach MEMORAe, we are interested in these learning forms. They are connected to the knowledge management practices and in this paper we present why and how we developed a learning environment based on the concept of learning organizational memory. This environment is a web platform using semantic annotations and Web2.0 technologies in order to organize, share and capitalize organizational competences, knowledge and resources.

The increasing number of available resources that may be used during e-learning scenarios can show problems of access, management and sharing of these resources. An e-learning application deals with the same problem of relevance to the Web. The semantic Web recommends standards for providing a solution to these issues. One main feature of this new Web generation is the use of semantics based on ontologies. This paper presents an approach for indexing learning resources by a specific LOM ontology. We also present the tool that we have built allowing to describe learning objects and helping to search for them.

As each university owns several web sites dedicated to face-to-face lectures support or distance learning training, courses can no more be considered as global presentations of theory elements. Organizations involving different actors, contribute to produce documents and information and doing so must share knowledge. We present in this paper a way to index documents inside a memory adapted to e-learning situations. This indexing mainly requires concepts representing the pedagogical notions of the domain of training. It requires also some concepts that are typical of learning activities.

We analyze a real example of collaborative work in mechanical design. Two kinds of problems emerge from this analysis: the first is a lack of complete design environment very early before modeling. The second is the need for a flexible and very intuitive collaborative environment. In addition, there is no knowledge management of the exchanges, and the design method could be improved with information assistance during the project. From this analysis, we try to detect design and collaborative tasks for which students need help. Then a personal assistant agent based on a task ontology is proposed to offer them this help.