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Relevant and readily available information is a crucial basis for decision making, problem solving or performing knowledge-intensive work. The motivation for this thesis lays in the fact that in certain situations not all information provided by an application is important and relevant to the end user. Modern enterprise information systems provide huge amounts of information and quite often, in such large volumes of data, users do not know what is important, why something is important and finally, how to find relevant information. Moreover, in complex business environments users may not be fully aware of the current situation which in turn can negatively influence the decision making process. So, it is very important to provide the appropriate information to the user considering the respective situation. However, even if the user is provided with this information, the problem is not essentially solved. The user also has to understand why the provided information is important which means that he/she has to comprehend the current situation or to be aware of the context in which this situation happened. In this way, the user becomes able to fully understand the real meaning of the information. Therefore, it has become crucial for enterprise applications to be aware of the context they are being used in. Nowadays, manufacturing applications collect and store various kinds of data and information. This data describes users as well as the various aspects of business. This means that if properly interpreted, this data could be used to describe the overall user and business context. However, this is no easy task as context data is subject to constant change and can be highly heterogeneous. This work proposes an approach to solve these problems by providing an ontology-based context model and rules for its interpretation. This model will classify with the help of OWL-built ontologies the context of the users (the employees of a business who are accessing the system) and the context of the business. The user and business context will allow for an enterprise application to anticipate which information is important and relevant in order to serve it to the appropriate user. Since the solution utilizes contextual information and provides information and services according to it, the proposed approach could thus be characterized as “context-aware”. In order to correctly model context information, various application domains in which a model could be applied should be taken into account. This basically means that it is necessary to provide a generic model which can be used in various application domains and use-cases. Having a generic model provides the distinct advantages of easy context information exchange as well as reusability of the model. On the other hand, it significantly reduces the expressiveness of the model and requires more computing to interpret it. Therefore, this approach proposes the upper model (ontology) which specifies only the common and generic concepts of context and supports context information exchange. However, it is not possible to model different domains only with the upper model, which means that for each case, a domain specific model should be specified. This domain specific model should extend the generic upper model and describe the specific domain in more detail. [...]