According to Knight, uncertainty signifies deviations from the expected states, which prevent us from the use of any probability for the determination of a result for a given action or decision [1]. This paper describes the phenomenon of uncertainty in the face of technological megatrends and challenges associated with them. The article focuses on the analysis of the uncertainty in one of the most important technology trends -the Internet of Things (IoT) -on the example of Healthcare. The right decisions are not always equivalent to good results. Sometimes, the decision taken in accordance with general rules brings worse results than the one who breaks them. Such a situation is possible as a result of the uncertainty accompanying the predictions of the future. In this article the concept of the IoT is treated as a big, complex, dynamic system with specific characteristics, dimensions. structures and behaviors. The aim of the article is to analyze the factors that may determine the uncertainty and ambiguity of such systems in the context of the development of Healthcare, and recommendations are made for future research directions.

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Dans un environnement dynamique, les ressources termino-ontologiques et les annotations sémantiques qu'elles permettent de construire doivent être modifiées régulièrement et en cohérence pour s'adapter à l'évolution du domaine sur lequel elles portent et des collections documentaires annotées. En support à un environnement d'annotation automatique de documents (TextViz), nous avons développé EvOnto pour faciliter l'évolution d'une ressource termino-ontologique en tenant compte des annotations sémantiques définies avec celle-ci. EvOnto permet de formuler une demande de changement, d'évaluer l'impact de ce changement sur la ressource termino-ontologique et sur les annotations sémantiques, et finalement de décider de la mise en oeuvre de ce changement. Cet article présente les principes d'EvOnto et une étude de cas qui illustre son apport à l'évolution d'ontologie.ABSTRACT. In dynamic environments, Ontological and Terminological Resources and semantic annotations built from them must be changed to adapt to domain evolutions and to new needs for annotated documents. Therefore, we developed the EvOnto tool that extends the TextViz tool for automatic document annotation with a termino-ontological resource. EvOnto is intended for the ontologist, it provides an interactive guide-line allowing him to formulate a change request, to evaluate its impact on the termino-ontological resource and on the semantic annotations, and finally to decide how the change will be implemented. Our paper presents the main principles of EvOnto and it reports a case-study that illustrates its support to ontology evolution.

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The ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant approach able to achieve automatic enrichment from a textual corpus or dataset of various topics. In this paper, we describe a new approach for automatic learning of terminological ontologies from textual corpus based on probabilistic models. In our approach, two topic modeling algorithms are explored, namely LDA and pLSA for learning topic ontology. The objective is to capture semantic relationships between word-topic and topic-document in terms of probability distributions to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and retrieving corresponding topic ontology for a user query demonstrates the effectiveness of the proposed approach.