Ambient Intelligence systems need to represent information about their environment and recognize relevant situations to perform appropriate actions proactively and autonomously. The context information gathered by these systems comes with imperfections such as incompleteness or incorrectness. These characteristics need to be handled gracefully by the Ambient Intelligence system. Moreover, the represented information must allow for a fast and reliable recognition of the current situation. To solve these problems we propose a method for situation modeling using the Description Logics based ontology language OWL DL and a framework for employing Description Logics reasoning services to recognize the current situation based on context. The benefits from the approach are manifold: the semantics of Description Logics allow for graceful handling of incomplete knowledge. The well-investigated reasoning services do not only allow recognizing the current situation, but also can add to the reliability of the overall system. Moreover optimized reasoning systems are freely available and ready to use. We underpin the feasibility of our approach by providing a case study based on a smart home application conducting an evaluation of different Description Logics reasoners with respect to our application ontology as well as a discussion of Description Logics systems in Ambient Intelligence.
Thomas et al. REF proposed to use the Description Logics (DL) based ontology language, OWL DL, to model situation.
IOS Press Employing Description Logics in Ambient Intelligence for Modeling and Reasoning about Complex Situations
JAISE
JAISE
Computer Science