Temporal data given by Alzheimer's patients are mostly uncertain. Many approaches have been proposed to handle certain temporal data and lack uncertain ones. This paper proposes an approach to represent and reason about quantitative time intervals and points and qualitative relations between them. It is suitable to handle certain and uncertain temporal data. It includes three parts. (1) The authors extend the 4D-fluents approach with certain components to represent certain and uncertain temporal data. (2) They extend the Allen's interval algebra to reason about certain and uncertain time intervals. They adapt these relations to relate a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) They propose a certain ontology based on the extensions. A prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer's patients to handle uncertain data inputs. The evaluation proves the usefulness of the approach as all the inferences are well established and the precision results are promising.

Over the last few years, NoSQL systems are gaining strong popularity and a number of decision makers are using it to implement their warehouses. In the recent years, many web applications are moving towards the use of data in the form of graphs. For example, social media and the emergence of Facebook, LinkedIn and Twitter have accelerated the emergence of the NoSQL database and in particular graph-oriented databases that represent the basic format with which data in these media is stored. Based on these findings and in addition to the absence of a clear approach which allows the creation of data warehouse under NoSQL model, we propose, in this paper, an approach to create a Graph-oriented Data warehouse. We propose the transformation of Dimensional Fact Model into Graph Dimensional Model. Then, we implement the Graph Dimensional Model using java routines in Talend Data Integration tool (TOS).

International audience

In view of the proliferation of audiovisual documents and the indexing limits mentioned in the literature, the progress of a new solution requires a better description extracted from the content. In this paper, we propose an approach to improve the description of the cinematic audiovisual documents. However, this consists not only in extracting the knowledge meaning conveyed in the content but also combining textual and visual modalities. In fact, the semiotic desription represents important information from the content. We propose in this paper an approach based on the use of pre and post production film documents. Consequently, we concentrate efforts to extract some descriptions about the use not only of the probalistic Latent Dirichlet Allocation (LDA) model but also of the semantic ontology LSCOM. Finally, a process of identifying a description is highlighted. In fact, the experimental results confirmed the importance of the performance of our approach through the comparison of our result with a human jugment and a semi-automatic method by using the MovieLens dataset.

The identification of alignments between heterogeneous ontologies is one of the main research issues in the semantic web.The manual matching of the ontologies is a complex, time consuming and an error prone task.Therefore, ontology matching systems aims to automate this process.Usually, these systems perform the matching process by combining element and structural level matchers.Selecting the optimal string similarity measure associated with its threshold is an important issue in order to enhance the effectiveness of the element level matcher, which in turn will improve the whole ontology system results.In this paper, we present POMap, an ontology matching system based on a syntactic study covering element and structural levels.For the element level matcher we have adopted the best configuration based on the analysis of the performances of many string similarity measures associated with their thresholds.For the structural level, we have performed a syntactic study on both subclasses and siblings in order to infer the structural similarity.Our proposed matching system is validated and evaluated on the Anatomy, the Conference and the Large Biomedical tracks provided by the benchmark of OAEI 2016 ontology matching campaign.

Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.

How to find and identify learning objects according with the learner profile represents a main interest in the quality of learning process. Thus, using the paradigm of Semantic Web Services ensure the independence and reusability of learning object in a different context. In this paper, we propose an extension of OWLS that encompass the description of the learning intention and the context of use that characterize a learning object. We also describe the generic scenario of the publication and discovery process.