The emergence of Information and Comunication Technologies (ICT) in the early 1990s, especially the Internet, made it easy to produce data and disseminate it to the rest of the world. The strength of new Database Management System (DBMS) and the reduction of storage costs have led to an exponential increase of volume data within entreprise information system. The large number of correlations (visible or hidden) between data makes them more intertwined and complex. The data are also heterogeneous, as they can come from many sources and exist in many formats (text, image, audio, video, etc.) or at different levels of structuring (structured, semi-structured, unstructured). All companies now have to face with data sources that are more and more massive, complex and heterogeneous.technical information. The data may either have different denominations or may not have verifiable provenances. Consequently, these data are difficult to interpret and accessible by other actors. They remain unexploited or not maximally exploited for the purpose of sharing and reuse. Data access (or data querying), by definition, is the process of extracting information from a database using queries to answer a specific question. Extracting information is an indispensable function for any information system. However, the latter is never easy but it always represents a major bottleneck for all organizations (Soylu et al. 2013). In the environment of multiuse of complex and heterogeneous, providing all users with easy and simple access to data becomes more difficult for two reasons : - Lack of technical skills : In order to correctly formulate a query a user must know the structure of data, ie how the data is organized and stored in the database. When data is large and complex, it is not easy to have a thorough understanding of all the dependencies and interrelationships between data, even for information system technicians. Moreover, this understanding is not necessarily linked to the domain competences and it is therefore very rare that end users have sufficient theses such skills. - Different user perspectives : In the multi-use environment, each user introduces their own point of view when adding new data and technical information. Data can be namedin very different ways and data provenances are not sufficiently recorded. Consequently, they become difficultly interpretable and accessible by other actors since they do not have sufficient understanding of data semantics. The thesis work presented in this manuscript aims to improve the multi-use of complex and heterogeneous data by expert usiness actors by providing them with a semantic and visual access to the data. We find that, although the initial design of the databases has taken into account the logic of the domain (using the entity-association model for example), it is common practice to modify this design in order to adapt specific techniques needs. As a result, the final design is often a form that diverges from the original conceptual structure and there is a clear distinction between the technical knowledge needed to extract data and the knowledge that the expert actors have to interpret, process and produce data (Soylu et al. 2013). Based on bibliographical studies about data management tools, knowledge representation, visualization techniques and Semantic Web technologies (Berners-Lee et al. 2001), etc., in order to provide an easy data access to different expert actors, we propose to use a comprehensive and declarative representation of the data that is semantic, conceptual and integrates domain knowledge closeed to expert actors.