In this paper, we propose an approach to extract ontological structures from datasets generated by health care users of social networking sites. The objective of this approach is to exploit the user generated implicit semantics as a complement to more formalized knowledge representations. We aim for this latter to leverage the adoption level of the Electronic Health Record systems that are complaining from the shortage in standards and controlled vocabularies.

Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.

The idea of using ontologies in the field of software engineering is not new. For more than 10 years, the Software Engineering community arouse great interest for this tool of semantic web, so to improve; their performance in production time and realisation complexity on the one hand, and software reliability and quality on the other hand. The standard ISO / IEC 24744, also known as the SEMDM (Software Engineering – Meta-model for Development Methodologies), provides in a global perspective, a conceptual framework to define any method of software development, through the integration of all methodological aspects related to the followed procedures, as well as, products, people and tools involved in the conception of a software product. The purpose of this article is to create domain ontology for ISO / IEC 24744 using an MDA process. This ontology will serve as semantic reference in order to assist for a better interoperability between the different users of the standard (human, software or machine).

Professional use of cloud health storage around the world implies Information-Retrieval extensions. These developments should help users find what they need among thousands or billions of enterprise documents and reports. However, extensions must offer protection against existing threats, for instance, hackers, server administrators and service providers who use people’s personal data for their own purposes. Indeed, cloud servers maintain traces of user activities and queries, which compromise user security against network hackers. Even cloud servers can use those traces to adapt or personalize their platforms without users’ agreements. For this purpose, we suggest implementing Private Information Retrieval (PIR) protocols to ease the retrieval task and secure it from both servers and hackers. We study the effectiveness of this solution through an evaluation of information retrieval time, recall and precision. The experimental results show that our framework ensures a reasonable and acceptable level of confidentiality for retrieval of data through cloud services.

The semantic web goal is to share and integrate data across different domains and organizations. The knowledge representations of semantic data are made possible by ontology. As the usage of semantic web increases, construction of the semantic web ontologies is also increased. Moreover, due to the monolithic nature of the ontology various semantic web operations like query answering, data sharing, data matching, data reuse and data integration become more complicated as the size of ontology increases. Partitioning the ontology is the key solution to handle this scalability issue. In this work, we propose a revision and an enhancement of K-means clustering algorithm based on a new semantic similarity measure for partitioning given ontology into high quality modules. The results show that our approach produces meaningful clusters than the traditional algorithm of K-means.

Web service selection is an indispensable process for web service composition. However it became a difficult task as many web services are increased on the web and mostly they offer similar functionalities, which service will be the best. User preferences are the key to retain only the best services for the composition. In this paper, we have proposed a web service composition model based on user preferences. To improve the process of web service composition we propose a case-based planning approach with user preferences which uses successful experiences in past to solve similar problems. In this paper we integrate user preferences in the phase of selection, adaptation and planning. Our main contributions are a new method of case retrieval, an extended algorithm of adaptation and planning with user preferences. Results obtained offer more than a solution to the user and taking both functional and non-functional requirements.

Recommender systems have shown great potential to help users find interesting and relevant Web service (WS) from within large registers. However, with the proliferation of WSs, recommendation becomes a very difficult task. Social computing seems offering innovative solutions to overcome those shortcomings. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as social networks, trust, reputation, and recommendation. In this paper, we propose a social trust-aware system for recommending Web services (WSs) based on social qualities of WSs that they exhibit towards peers at run-time, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results and demonstrating that our service recommendation method significantly outperforms conventional similarity-based and trust-based service recommendation methods.

Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.