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Abstract

Data on observed and forecasted environmental conditions, such as weather, air quality and pollen, are offered in a great variety in the web and serve as basis for decisions taken by a wide range of the population. However, the value of these data is limited because their quality varies largely and because the burden of their interpretation in the light of a specific context and in the light of the specific needs of a user is left to the user herself. To remove this burden from the user, we propose an environmental Decision Support System (DSS) model with an ontology-based knowledge base as its integrative core. The availability of an ontological knowledge representation allows us to encode in a uniform format all knowledge that is involved (environmental background knowledge, the characteristic features of the profile of the user, the formal description of the user request, measured or forecasted environmental data, etc.) and apply advanced reasoning techniques on it. The result is an advanced DSS that provides high quality environmental information for personalized decision support.

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... In order to achieve this, they propose a semiautomatic method of constructing multilingual ontologies, as well as a semantic searching mechanism based on concept similarity. In another approach [11] the authors present a system that provides high quality environmental information for personalized decision support based on reasoning. ...
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Applications in pervasive computing environment exploit information about the context of use, such as the location, tasks and preferences of the user, in order to adapt their behavior in response to changing operating environments and user requirements. Utilizing context with aid of ontology in data and model, decision support systems can provide better and more desirable support to their users. The effects and advantages of exploiting ontology in user modeling and DSS is discussed. We propose a framework of a ontology-based decision support system (O<sub>2</sub>DSS), including ontology in model base, database and their advantages.
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