Purpose -The purpose of this paper is to present the MEMORAe project, the goal of which is to offer an alternative to the loss of competencies and knowledge in an organization.Design/methodology/approach -Within the project MEMORAe, interest was focused on the capitalization of knowledge and competencies in the context of an organization. The E-MEMORAe environment was developed based on the concept of learning organizational memory. This environment is dedicated to be used by a semantic learning organization as support for competency-based training. It is evaluated in this context.Findings -In the E-MEMORAe environment, learning content is indexed by knowledge and competencies organized by means of ontologies. Learners can acquire thise knowledge and these competencies by doing different tasks, accessing different contents. In the memory, competencies are defined via the knowledge they enable to be put into practice.Practical implications -It is known that some industrial communities of practice are interested in the use of E-MEMORAe.Originality/value -Within the MEMORAe project, an ontology-based learning organizational memory is proposed as support for learning object retrieval by competency for competency based learning. Using such a memory enables and goes beyond organizational knowledge management. Knowledge and competencies are defined and structured to facilitate their access and their learning. This latter is also made possible thanks to the resources that they index.2. Loss of know-how or competencies. This loss can take place in time (retirement, mutation, etc.). It can also take place through space when know-how and competencies are used only in one site but not in the other sites of the company.A competence is a way to put into practice some knowledge in a specific context. From an educational point of view, knowledge is defined as all the notions and the principles that a person acquires through study, observation or experience which can be integrated into skills. However, studying an encyclopedia is not sufficient to gain knowledge; didactic work has to be done.

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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.

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 ontologybased 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.

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.

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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.

Working collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. It is therefore necessary to consider the criteria to be taken into account to promote its effectiveness. As part of our work, we are interested in taking into account the collaboration context for this purpose and the role that contact can take in this context. We wondered about its definition, representation and exploitation. The latter must be able to be done at different stages of collaboration: before, during and after. In this article, we present the collaboration context model that we have established and show why and how it can be used to establish successful collaboration.