An Intelligent Technique for Extracting Subjects from User Profile Using ODP Ontology-Driven Reasoning

Abstract: Nowadays, the amount of available information, especially on the Web, is increasing. In this field, the role of user modeling and personalized information access is obviously vital. The traditional techniques like BOW (Bags of words) limit recommendations to the words which have been stored in the profile. In other words, the news items, which semantically relate to the users interests, can't be recognized and recommended to the users. Besides, BOW technique suffers from the curse of dimensionality, thus computational burden reduction is an essential task to efficiently handle a large number of terms in practical applications. This study focuses on the problem of choosing a representation of documents that can be suitable to induce concept-based user profiles as well as to support a content-based retrieval process. In this study, a new approach has been proposed to construct a ranked semantic user profile through extracting the related subjects. The new items can be recommended through collecting information from the user's selections, based on existing domain ontology ODP. The efficiency of the proposed technique has been shown by embedding it into an intelligent aggregator, RSS (RSS is acronym of " Really Simple Syndication) feed reader, which has been trained and evaluated by different and heterogeneous users. The results in experimental session show that the incoming news item which semantically relates to the profile gets highly recommended to the user despite its excluding of common words in the profile

Effect of an Ergonomics Educational Program on Musculoskeletal Disorders in Nursing Staff Working in the Operating Room: A Quasi-Randomized Controlled Clinical Trial

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Integration of Heterogeneous Modeling Languages via Extensible and Composable Language Components

Effective model-driven engineering of complex systems requires to appropriately describe different specific system aspects. To this end, efficient integration of different heterogeneous modeling languages is essential. Modeling language integaration is onerous and requires in-depth conceptual and technical knowledge and ef- fort. Traditional modeling lanugage integration approches require language engineers to compose monolithic language aggregates for a specific task or project. Adapting these aggregates cannot be to different contexts requires vast effort and makes these hardly reusable. This contribution presents a method for the engineering of grammar-based language components that can be independently developed, are syntactically composable, and ultimately reusable. To this end, it introduces the concepts of language aggregation, language embed- ding, and language inheritance, as well as their realization in the language workbench MontiCore. The result is a generalizable, systematic, and efficient syntax-oriented composition of languages that allows the agile employment of modeling languages efficiently tailored for individual software projects.Comment: 12 pages, 11 figures. Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development. Angers, Loire Valley, France, pp. 19-31, 201